Towards an Automated Medical Diagnosis System for Intestinal Parasitosis

Journal Pre-proof Towards an Automated Medical Diagnosis System for Intestinal Parasitosis Beaudelaire Saha Tchinda, Michel Noubom, Daniel Tchiotsop, Valerie Louis-Dorr, Didier Wolf PII:

S2352-9148(19)30242-4

DOI:

https://doi.org/10.1016/j.imu.2019.100238

Reference:

IMU 100238

To appear in:

Informatics in Medicine Unlocked

Please cite this article as: Tchinda BS, Noubom M, Tchiotsop D, Louis-Dorr V, Wolf D, Towards an Automated Medical Diagnosis System for Intestinal Parasitosis, Informatics in Medicine Unlocked, https://doi.org/10.1016/j.imu.2019.100238. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier Ltd.

Towards an Automated Medical Diagnosis System for Intestinal Parasitosis

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Beaudelaire Saha Tchinda¹, Michel NOUBOM2, Daniel Tchiotsop¹, Valerie Louis-Dorr3, Didier Wolf3 ¹Laboratoire d’Automatique et d’Informatique Appliquée (LAIA), IUT-FV de Bandjoun, Université de Dschang-Cameroun, B.P. 134 Bandjoun. 2

Département des sciences Biomédicales, Faculté de Médecine et des Sciences Pharmaceutiques, Université de Dschang-Cameroun. 3

Centre de Recherche en Automatique de Nancy (CRAN), UMR CNRS 7039, ENSEM, Université de Lorraine, Nancy-France.

Abstract Human parasites are a real public health problem in tropical countries, especially in underdeveloped countries. Usually, the medical diagnosis of intestinal parasites is carried out in the laboratory by visual analysis of stool samples using the optical microscope. The parasite recognition is realized by comparing its shape with known forms. We offer a solution to automate the diagnosis of intestinal parasites through their images obtained from a microscope connected directly to a computer. Our approach exploits the contour detection based on the multi-scale wavelet transform for detecting the parasite. Active contours are combined with the Hough transform to perform image segmentation and extraction of the parasite. We used principal component analysis for the extraction and reduction of features obtained directly from pixels of the extracted parasite image. Our classification tool is based on the probabilistic neural network. The obtained algorithms were tested on 900 samples of microscopic images of 15 different species of intestinal parasites. The result shows a 100% recognition rate of success. Keywords: Intestinal Parasites; wavelet Edge detector; Hough Transform; Active contours; Probabilistic Neural Network.

1. Introduction The parasite is an organism that lives at the expense of its host, which is also an organism. Intestinal parasites are a form of human parasitosis. Approximately four billion people are affected in the world [1]. Intestinal parasites are responsible for physical or behavioral disturbances in children, in immuno-deficient persons, and in worst cases, can cause death. The diagnosis of intestinal parasites is carried out in the laboratory by observing stool samples through an optical microscope. The parasite identification is done by comparing the shape observed with known forms. This practice is time consuming and laborious. In addition, this clinical test is slow and prone to many errors of diagnosis. There is no reliable quality control. The aim of our research in this article is to contribute to solving these problems. In the literature, we found some studies devoted to the medical diagnostics of intestinal parasites based upon microscopic image analysis. Parasitic organisms have at certain stages of development, well known morphologies. They therefore lend themselves to pattern recognition techniques. Various approaches in the literature can be distinguished either by the parasite species involved in classification, or by the type of characteristics used by the classifier. In [2], the authors were interested in identifying human helminthes eggs through artificial neural networks (ANN). Widmer et al. [3] focused on the recognition of Giardia cysts and Cryptosporidium oocysts using the artificial neural network and immunofluorescence microscopy. Using Bayesian classification, Castanon et al. [4] identified seven species of Eimeria. Ginoris et al. [5, 6] used an artificial neural network to recognize metazoa and protozoa, that are commonly found in the mud. Dogantekin et al. [7] and Avci et al. [8] used support vector machines (SVM) and a fuzzy inference system based on an adaptive network, to recognize helminthes eggs. In [9], the authors proposed a diagnostic method for roundworm and whipworm eggs using the parameters of shape, roundness and dimension. Their classifier is based on a filtering system with determination of stable thresholds. However, these studies do not address the identification of human intestinal protozoa, and the parasites segmentation step is manual. Suzuki et al. [10] proposed a significant advance towards the automation of parasites diagnosis by image analysis. Their approach is the first to focus on 15 species of protozoa and helminthes among the most common in Brazil. The image analysis method used in [10] has three main steps: the image segmentation that locates and delimits objects present in the image; the description of the forms that extracts the features of the segmented object; the classification that uses these features to perform the recognition of the species of parasites. These features include the parameters of roundness, geodesic distances, curvature variance, texture, perimeter, and area of the parasite. The classifier used in [10] is based on the image foresting transform (IFT) method. A performance analysis of algorithms for the identification of intestinal parasites is proposed in [11]. In [12], the

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authors proposed a system for the identification and quantification of pathogenic helminth eggs using a segmentation by the watershed

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Figure 1- Flowchart of the proposed system.

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The microscope provides a representation of what is not visible to the naked eye. An optical microscope coupled to an acquisition sensor was used in our study. It will permit us to access the image of parasites for recognition. Images of stools are captured via microscope and transferred to computer for analysis and diagnosis, as shown in Figure 2. To acquire stool images, we used glass slides and microscopy slides; an optical microscope with digital camera eyepiece and PC plug: "TRAVELER MICROSCOPE", with its illuminated pen, PC installation drivers and a video capture program; a laptop: Intel (R) Pentium (R) M 2.26GHz processor, 504MB RAM, Microsoft Windows XP SP1, 55GB hard disk with more than 5GB free, a HI color display screen (1024 x 768 pixels). Note that the characteristics of our computer are much higher than the minimum required for this microscope

method. Their classification system uses the nearest neighbor algorithm. This analysis remains limited to the eggs of two species of helminthes (roundworms and whipworms). Saha et al. proposed in [13] the first and partial solution devoted to the recognition of 9 species of amoebic cysts. In this paper, a complete system for automated medical diagnosis of human intestinal parasites is proposed. As well as in [13], our method is based on the processing of microscopic images and an artificial neural network system. The steps of our approach are edge detection, image segmentation and pattern recognition. One of the main novelties of our method over the existing techniques is that the step of detection and extraction of parasites in microscopic images is fully automated. Another difference of our method over previous methods of parasite diagnosis is the descriptor type of characteristics that we used. Our features descriptor directly uses the pixel of the image and does not need to compute other parameters. Our identification tool uses principal components analysis (PCA) in the reduction of dimensionality and probabilistic neural network for classification. In addition, the varieties of intestinal parasites are extended to 15 in this work. Also, the results of the different steps of our system are detailed and the models used are justified here. In the next section, we present the equipment and methodology used. This requires image acquisition, segmentation and recognition. In section III, the experimental results are provided with detailed discussion. This is followed by a conclusion in section IV.

2.

Materials and Methods

The main objective of our system consists in identifying and recognizing the varieties of human parasites in feces slides. For this purpose, we designed a system whose functioning is given by the block diagram illustrated in figure 1. After acquiring microscopic stools images, our system makes image segmentation and extracts the parasites. Classification features are then extracted from the image. These features are input to the neural network. After training and testing, the classifier provides the result of the recognition by displaying the type of parasite detected.

Microscopic stools images acquisition

Segmentation and extraction of Parasite: - Contours Detection using wavelet transform - Regions of interest localization with the Hough Transform - Final contour of parasite localization with active contours - Parasite extraction through binary logic operations.

Feature extraction: Projection in the base of the PCA

Classification: -Training of the Probabilistic Neural Network - Network Test

2.1. Acquisition of the microscopic images of stools

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namely: Pentium III 800Mhz or more, recommended for digital video (DV), 256MB RAM or more, Microsoft Windows 98 SE, Windows Me, Windows 2000 or Windows XP; at least 500 MB of available hard disk memory (4GB is recommended); a True Color or HI color display (1024 x 768 pixels). The microscopic images of stools that were used were obtained in a parasitology laboratory of the Public Regional Hospital and in a private clinical laboratory “Clab-Labo” both in the town of Bafoussam- Cameroon. More images of parasitized stools were obtained from databases available online [14 - 15].

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Figure 2- The acquisition materials consisting of a microscope directly connected to a computer.

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Generally, a microscopic image of stools contains many unnecessary items for diagnosis. In addition, there are many parasites in a single image. Then, before its recognition, each parasite must be individually extracted. The parasite extraction is done through the process of segmentation. Segmentation techniques are either region based or contours based methods. Firstly, methods based on the region use the intrinsic properties of objects that are to be extracted. These methods depend heavily on image characteristics and the shape to be extracted. The second category of methods is based on the outline that seeks the contours of the objects to be extracted by using the discontinuity of the image intensity. The edge detection processes either the maxima of gradient or the zero crossing of the Laplacian of the image intensity function. In [16], Tchiotsop et al. have shown that edge detection using multi-scale wavelet produces better results than other conventional edge detectors, and particularly when it is applied on microscopic images of feces. The main advantage of the multi-scale wavelet transform in edge detection is the ability to choose the size of the details that will be detected. Nevertheless, the outlines obtained are often split. The Hough transform can extract parametric shapes in an image. It is used in computer vision problems such as the detection of lines, circles, ellipses or other curves. The Hough transform is successfully used on ultrasound images by Golemati et al. [17] to highlight transverse and longitudinal sections of the carotid artery. The form of certain intestinal parasites such as amoebic cysts is circular. Thus, a circular model of the Hough transform can be easily used for their detection and extraction. Meanwhile, other irregular shapes will not be totally located. The active contours technique, also called the snake model, is another segmentation approach. It is very effective in contours detection. An implementation example of this method is presented in [18]. The high dependence of active contours to the initial contour is a major disadvantage. When the initial outline is near to the target contour, the active contours algorithm converges quickly. The combination of snake model with the Hough transform is feasible. The Hough transform allows one to automatically locate the parasite region of interest. This computational result is then considered as the initial outline for the active contours. In [19], this method is successfully utilized in the extraction of human parasites on microscopic images of stools. Segmentation uses the outline map of the image to separate the parasite from its background. This image background of the parasite is next deleted using a logic operation. Thus, our segmentation algorithm uses the active contours combined with the Hough transform. The multi-scale wavelet transform is used to detect the edges of the parasites.

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2.2. Segmentation and extraction of the parasite

2.2.1. Principle of edge detection based on the multi-scale wavelet transform It is shown in [20] that the wavelet transform

θ ( x, y )

Wa f ( x, y ) of an image f ( x, y ) is proportional to the first derivative

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smoothed by a convolution kernel

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∂   ∂x ( f ∗θa )( x, y )  Wa1 f ( x, y )   = a∇ ( f ∗θa )( x, y ) Wa f ( x, y ) =  2  = a W f ( x, y )  ∂    a   ∂y ( f ∗θa )( x, y )   

at a given scale a . This proportional relationship is given by the following equation:

(1)

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133

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1  x y a a a

with θa ( x, y ) = θ  ,  To locate the edges on an image, the wavelet transform module is first computed. Its local maxima is then determined in the direction of the gradient ∇ (

f ∗ θ a )( x, y ) . The modulus-angle representation of the wavelet transform, at each scale a ( a = 2j , j ∈ )

is defined in [16, 20] by the following equation: 2 2  1 2  M 2 j f ( x, y ) = W2 j f ( x, y ) + W2 j f ( x, y ) ,  (2)   W 2j f ( x, y )   A2 j f ( x, y ) = tan −1  21   W j f ( x, y )    2  M 2 j f ( x, y ) is the modulus of the wavelet transform. A2 j f ( x, y ) is the angle between the gradient

( f ∗θ ) ( x, y ) and the X-axis of the image plane (x, y). For the convolution kernel θ ( x, y ) , we used

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vector ∇

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function, which offers algorithmic advantages [16]. The edge detection based on the multi-scale wavelet transform consists to firstly detect, at each scale, the local maxima of the modulus of the wavelet transform. For local maxima detection, a hysteresis threshold is used. This kind of threshold is defined by an interval [TL, TH], with TL=0.4*TH. Afterwards, these local maxima are chained from the coarser scale (a>1) to the finest scale (a=1). The goal of the chaining is to solve the location problems caused by the convolution at the large scale of the analysis. The full version of the contours detector based on the multi-scale wavelet transform can be found in [16].

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2j

the Gaussian

2.2.2. Principle of Hough transform The Hough transform (HT) is a tool used to determine significant groups of characteristic points that meet some parametric constraints. It is based on the general principles defined by parametric constraint of the form of the following equation: (3) f ( X , A) = 0 where X = ( x1 , x2 ,...xn ) is a point of the image and A is a vector of parameters. T

The constraints can represent curves (lines, circles, etc.), the surfaces (planes, cylinders, etc.) or the movement trajectories (of translation, rotation, etc.), depending on the interpretation of the feature point. For our case, the feature points are edge pixels obtained from the multi-scale wavelet transform. However, they could also be of the gray level values. We are interested to the detection of circular shapes in the image. In Cartesian coordinates, the equation of a circle is given as follows:

( x − a) + ( y − b) 2

2

= r2

(4)

where ( a, b) represents the coordinates of the center and r is the radius of the circle. In the parametric constraints of equation (3), the parameter vector is given by equation (5)

A = ( a , b, r ) Here, X = ( x, y ) is a point of the image.

(5)

For each pixel in the contour, we want to know if this pixel belongs to a circle. Thus we find the place for parameters of that circle. This can be seen from equation (4) that x and y are considered to be fixed points, while a, b and r are varying. The basic method uses a three-dimensional accumulator array A(a,b,r). Each contour element vote for all the circles to which it may belong and the peak of the accumulator array A (a,b,r) is sought. This peak gives the position of the circle and its radius. If it happens that we know the radius in advance, we will only need a 2-D array accumulator. The full version of the Hough transform algorithm can be found in [19] and references therein.

2.2.3. Contour optimization through the Gradient Vector Flow (GVF) active contours The method of active contours, also called a snake, is based on the construction of an energy functional that measures the relevance of the outline. The model of the Gradient Vector Flow (GVF) is one of the snake models that was developed in order to increase the capture range and improve the ability of the snake model to move within the limits of concavities. The GVF model addresses these problems by introducing a new external force. The Gradient Vector Flow field is defined to minimize the following energy functional [21]:

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{

E = ∫∫ µ. ( ux2 + u y2 + vx2 + v2y ) + ∇f . V −∇f 2

2

}dx.dy

(6)

where the vector field is V(x,y)=(u(x,y),v(x,y)), f is the edge map of the image, ∇ f = ( f x , f y ) is the gradient of the image edge map.

The regularization parameter denoted by µ controls the relative effect of the two terms. µ is set in function to the amount of noise present in the image (the more µ is increased, the greater is the noise level). For our case, a value of 0.2 was used for µ. When we minimize the energy functional of Equation (6), the following Euler independent equations are derived [22]:

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 µ .∇ 2u − ( u − f x ) . ( f x 2 + f y 2 ) = 0   2 2 2  µ .∇ v − ( v − f y ) . ( f x + f y ) = 0 Equations (7) are resolved by considering u and v as functions of time. We get:

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Vt =

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( ut ( x, y, t ), vt ( x, y, t ) ) , with: u ( x, y , t ) = µ .∇ u ( x, y, t ) − ( u ( x, y, t ) − f ( x, y ) ) . f ( x, y ) + f ( x, y ) ( )   v ( x, y, t ) = µ .∇ v ( x, y, t ) − ( v ( x, y, t ) − f ( x, y ) ) . ( f ( x, y ) + f ( x, y ) )  2

2

t

x

x

t

2

y

2

2

y

x

(7)

(8)

2

y

where fx and fy denote the derivative of f with respect to x and y respectively. The gradient vector flow is the solution of the system of equations (8). Since these equations are decoupled, they can be resolved separately in u and v, as scalar partial derivatives equations. After calculating the Gradient Vector Flow, the snake GVF is defined in [21] as the parametric curve

X ( s ) = [ x ( s ), y ( s ) ] satisfying the following dynamic equation: X t ( s , t ) = α . X '' − β . X '''' + k .V ( X )

(9)

Here, X is a function of time t and space s. β is the rigidity parameter and α is the tension parameter of the snake. The parameter k controls the extent to which the GVF field affects the deformation of the curve. X '' and X '''' designate the second and fourth derivatives of X with respect to s, respectively. After its discretization, the dynamic equation (9) is solved by iteration. From its initial curve, the iterative equations for the deformation of the snake are given in [21] by:

 xt = ( A + γ .I )−1 ( γ .xt −1 + k.u ( xt −1 , yt −1 ) ) X t = ( xt , yt ) =  −1  yt = ( A + γ .I ) ( γ . yt −1 + k.v( xt −1 , yt −1 ) )

(10) where x and y denote the coordinates of the vectors of points on the curve, γ =1/∆t is the step size of the iteration, I is an identity diagonal matrix and A is a pentadiagonal matrix with boundary conditions established for the snake used for longitudinal images. The snake is dynamically reparameterized after each iteration to maintain a separation point in the limit of 0.5-1.5 pixels [22]. The outlines search procedure by the snake method depends on other mechanisms such as interaction with a user or mechanism of high-level vision of the computer. First, the snake is placed near the contour of the region of interest. To achieve this first step, we used the Hough transform to automatically find the initial curve of the snake. The second step is the deformation of the initial curve (circle given by HT). This initial curve is considered as a snake that uses the GVF field as the external force. Details of the extraction algorithm is given in [19].

2.3. The feature extraction using principal components analysis (PCA) The principal components analysis (PCA) is a conventional linear method of feature extraction. It is based on the second order statistical analysis of the data, and in particular the analysis of eigenvalues of the covariance matrix. The basic idea is that in many

m observational variables x1 ,..., xm can be well represented by a parametric surface of n dimension y1 ,..., yn , with n smaller than m . such measures, the

PCA has been used in the dimensionality reduction of the multi-variable data [23 - 25]. It consists of projecting the data in the directions of their maximum variability. Thus, a family of variables is replaced by new variables of maximum variance, uncorrelated and which are linear combinations of the original variables. The principal components are basis vectors of the directions in descending order of variability. The basic vector for the direction of the highest variability is provided by the first principal component. The second principal component gives the basis vector for the next direction orthogonal to the first principal component, and so on. The calculation of the principal components requires computation of the covariance matrix, and calculation of eigenvalues, with storage of eigenvectors according to the descending order of eigenvalues. For the reduction of features, only the n (with n p m ) first

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eigenvectors will be selected, corresponding to the largest eigenvalues. The projection of the data in the new basis defined by the principal components is carried out by the scalar product of original data with the arranged eigenvectors. Practically, the PCA consists to look for a transformation of matrix W that corresponds to each characteristic vector

C X defining in the set X another vector of characteristics CY for the set Y, such that the covariance matrix of the elements in Y is diagonal. This transformation is linear and is defined as follows:

CY = CX W T The matrix W can be found by solving the following equation:

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ΣX wi = λi wi ,

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λi

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(11)

(12)

defines the eigenvalues and Since the eigenvectors

wi defines the eigenvectors. Σ X is the covariance matrix of X.

wi are known, the transformation matrix W is obtained by considering the wi as its columns. For the

reduction of the characteristics, W is a matrix of dimension m × n, containing n eigenvectors ( wi , i

= 1...n ) which correspond to the

n largest eigenvalues. For our case, m=144 and n=2.

2.4. Principle of recognition and classification using the probabilistic neural network The artificial neural networks (ANN) are very popular tools for pattern recognition. Probabilistic neural networks (PNN) are a special type of radial basis neural network. They are an alternative type of radial basis network geared toward classification problems. Ease of learning and the instantaneousness of the process are the main advantages of PNN [26 - 29]. In our work, we used a probabilistic neural network. Its architecture is given in figure 3. At the entrance of the network, there is a column vector P of R lines. The radial inner layer subtracts the input vector from the weight vector W of this layer, and we obtain W-P. The resulting vector is next multiplied element by element with the bias vector b. The obtained output S1 is used as the argument of the radial function "radbas"; thus we obtain a. The activation function in the radial inner layer is defined by

radbas( n) = exp(− n 2 ) . The output a is multiplied by the weighting matrix of the competitive layer LW, and S2 is obtained. The output S2 serves as an argument for the competitive function C. The competitive layer directs each entry in one of the K classes used in the learning step. The output S of the competitive function is a column vector of K lines. The competitive function C produces a 1 corresponding to the largest component of S2, and 0 otherwise.

W11 P

W21

WR1

W −P

C

1

Kx1

¶ .*

s1

radbas

S

a

LW

s2

b

Rx1 Input

Radial Layer

Competitive Layer

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Figure 3- Architecture of the probabilistic neural network.

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For training and testing the probabilistic network, the reduced characteristics are applied to the input. In our case, a total of 1800 images of microscopic parasites are used. These samples were divided into two sets of 900 images each. The first set, constructed from 60 images for each of the 15 types of parasites, was used for training. The second set of 900 images was considered for testing, with the same allocation by type of parasite. The weight matrix W of the radial inner layer is a Q × R matrix formed from training samples. W contains in each line R principal components of a training sample. 900 samples are available for training in this

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work, thus Q = 900. The radial layer bias are all set to − ln 0.5 / s , where s denotes the network spread constant. In our case, the spread constant has been adjusted to 0.4 by experience. This is the value that yielded an improved classification rate. The weight matrix of competitive layer LW is set to a K × Q matrix of Q vectors of target class. K is equal to 15 in our case.

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3. Results For our experimentation, we used microscopic images with a magnification of ×400 for helminthes eggs and ×1000 for amoeba cysts. The image format is JPEG and the size is 360 × 400. An example of a microscopic image of feces is showing in Figure 4. This image contains the parasitic elements and many other insignificant items. In this image, there is a round egg shape of a tapeworm. This egg is approximately 30 microns in size. A second egg found, of ovoid form, is from the whipworm, having a size of about 50 microns. One can also note the presence of food debris. Our goal is to detect the parasite, then extract and recognize it. Before the recognition phase, we need another important intermediate step, which is feature reduction. Its role is to facilitate recognition by reducing the number of entries in the classification system.

Tapeworm egg

Whipworm egg

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Food debris

Yeast

Figure 4-microscopic image of stool. This image contains a tapeworm egg, an egg of whipworm, yeast and other insignificant items such as food debris.

3.1 Results of the parasite detection

(A)

(B)

(C)

(D)

(E)

(F)

(G)

(H)

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Figure 5- Analysis scale variation and edge detection by the wavelet transform. (A) Contours analysis at scale 1 with a thresholding of [0.08, 0.2]; (B) Contours at scale 4 with a threshold of [0.08, 0.2]; (C) Contours at scale 6 with a thresholding of [0.08, 0.2]; (D) Contours at scale 8 with a threshold of [0.08, 0.2]; (E) Contours at scale 1 with a threshold of [0.12, 0.3]; (F) Contours at scale 4 with a thresholding of [0.12, 0.3]; (G) Contours at scale 6 with a threshold of [0.12, 0.3]; (H) Contours at scale 8 with a threshold of [0.12, 0.3].

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We applied the proposed edge detector to a large number of microscopic images of feces containing human parasites. Here the results obtained on some of these images were presented. Figure 5 shows the results obtained by applying the edge detection algorithm on the microscopic image of stools described above in Figure 4. We can note in Figure 5 that the variation of the analysis scale of the wavelet transform reveals the different structures present in the stools image. For example, as can be seen in Figure 5, on the images (A) and (E), for the two thresholding values used ([0.08, 0.2] and [0.12, 0.3]) , the results obtained at scale 1 of the wavelet transform

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are still confused and contain many unnecessary edges. However, in image panels (C), (D) (G) and (H), the results obtained at scales 6 and 8 with the same thresholdings correctly locate almost any desired edges (D). Thus, in this case, the scales 6 and 8 are better than scale 1 for the edge detection of parasites on the considered image. The threshold variation and the variation of analyzing scale can therefore reveal the various features of the parasite. Other results of the application of the multi-scale wavelets transform, and its performance on the edge detection of intestinal parasites in stool microscopic images, can be found in [16]. These results of the edge detection show the effectiveness, and justify the choice of this algorithm in the parasite detection.

3.2 Results of the parasite extraction

(A)

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Figure 6-Extraction of different parasites by varying the analysis parameters. (A) Original image. Extraction results of the parasite: (B) at the scale 4, with the threshold of [0.06, 0.15], and the radius of 43 pixels; (C) at the scale 4, with the threshold of [0.06, 0.15], and the radius of 51 pixels; (D) at the scale 6, with the threshold of [0.07, 0.18], and the radius of 44 pixels; (E) at the scale 8, with the threshold of [0.06, 0.15], and the radius of 41 pixels; (F) all four extracted parasites colored on the same initial image.

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We applied our segmentation algorithm based on the Hough transform and active contours on microscopic images of stools containing parasites, to detect and extract the parasites. Figure 6 shows the extraction of various parasite cysts of Entamoeba coli. These cysts may have a spherical or ovoid form, and are about 15 to 25 micrometers in diameter, depending upon maturity. Figure 6 (A) shows an image with 7 cysts. In panels (B), (C), (D) and (E), the parasites are extracted individually after the adjustment of analysis parameters (scale, threshold and radius of the circular Hough transform). Figure 6 (F) shows all four extracted parasites, colored on the same initial image. By varying the analysis parameters, all cysts present in this image can be extracted. In Figure 7, we can also observe other parasites extracted automatically from an initial outline obtained through the Hough transform. The images (AB ... F) are the microscopic images of feces to analyze. (A1-B1-F1 ...) show the edge images obtained from the multi-scale wavelet process (in black on the figure), on which are superimposed the initial contour given by the Hough transform (blue). They are obtained respectively with the scale, high threshold TH and radius parameters of (4, 0.6, 50 pixels) for A1; (2, 0.5, 40 pixels) for B1; (8, 0.75, 75 pixels) for C1; (6, 0.65, 80 pixels) for D1; (8, 0.4, 35 pixels) for E1; (8, 0.6, 60 pixels) for F1 and (8, 0.6, 35 pixels) for G1. The extracted parasite resulted from the convergence of the active contours is given in the images (A2-B2 ... F2) of the same figure. The analysis radius used by the Hough transform is related to the size of the parasite. It is expressed in pixels. It also depends on the resolution of the camera used, and the magnification of the microscope. Thus, a specific type of parasite can be searched automatically using its size.

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(A)

(A1)

(A2)

(B)

(B1)

(B2)

(C)

(C1)

(C2)

(D)

(D1)

(D2)

(E)

(E1)

(E2)

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Figure 7- Initial contours and parasites extracted. (A-B ... F) are microscopic images of feces for analysis. (A1-B1…F1) are the outlines obtained from the multi-scale wavelet transform, which are superimposed on the initial contours given by the Hough transform. (A2-B2 ... F2) are the images of the parasites extracted.

320 321 322 323 324 325

For the complexity reduction of the classification and recognition phase, the features using as input need to be reduced. The image of the extracted parasites are first resized. The image dimensions of the extracted parasite have been reduced to the 12 × 12 size. The resizing uses the Bicubic interpolation method. With this method, the output pixel value is a weighted average of pixels in the nearest 4-by-4 neighborhood. The PCA is subsequently used for projecting the characteristics of 12 × 12 pixels in a new space. The role of PCA is to reduce the size of the initial data space to the smallest intrinsic dimension. The obtained features space are

3.3 Result of the features extraction and dimensionality reduction

9

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necessary to economically and efficiently describe the data. For this goal, the dimension of the space of the new features is selected according to the classification accuracy. It also depends upon the system complexity.

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Figure 8- Parasite images of 15 classes and 20 samples each. For each image of parasites extracted, other images are generated by introducing noise. Other images are also generated by projecting the image on itself from any angle.

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Figure 8 shows a matrix containing a set of 300 images of parasites. We can distinguish 15 species of parasites. For each species, there are 20 samples. These samples are used to configure our system. Table 1 shows the result of the implementation of the PCA on this set of parasites. Those are the first three principal components (PCA1, PCA2 and PCA3). The values in this table are the average and the maximum deviation obtained on the 20 samples of each class (A, B, C, D, E, F, G, H, I, J, K, L, M, N, O). To facilitate classification, a satisfactory feature reduction paradigm would provide a set of new features with close values for the same class, and dispersed values for different classes. To evaluate our feature reduction tools, independent component analysis (ICA) was also applied to the same image samples. The ICA is a non-linear method of feature reduction [30]. Table 2 shows results for ICA. Also, 3 components are retained (ICA1, ICA2 and ICA3). When analyzing these tables, we can see the wide disparity of values for the

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same species of parasite, when it is based upon the ICA method (table 2). For the PCA, the values of the same class are fairly close together (Table 1). This observation can also be shown in Figures 9 and 10. In Figure 9, which provides the values of the first two components, for every 300 samples of 15 classes for the ICA, there is no parasite class regrouped. The dispersion rate is very high. For the PCA, shown in Figure 10, we can distinguish the fifteen types of parasites, each by a mode of representation. In addition, as shown in this figure, parasites of the same species are grouped together and are distant from other species. The capacity of the new features for the discrimination of the 15 classes can thus be evaluated qualitatively. The separation boundaries of the different classes of parasites can be readily obtained.

346 347 348 349

Table 1- Average values of the first three components of the PCA obtained for 15 classes of 20 samples of parasites. Different mean values are added to the maximum dispersion of these values compared to the average. In this case, we can see that the differences are quite small relative to the value considered. Similarly, there are significant differences between the different average values. This facilitates the classification phase.

Classes

PCA 3 -757.75±9.74 -962.86±22.82 -853.47±17.70 -936.07±23.24 -975.45±14.66 -966.03±9.06 -928.81±32.05 -966.88±15.91 -1007.37±8.29 -896.10±28.87 -760.68±14.03 -889.07±39.70 -888.28±16.96 -988.66±9.14 -1028.40±13.09

Table 2- Average values of the first three components of the ICA obtained for 15 classes of 20 samples of parasites. Different mean values are added to the maximum dispersion of these values compared to the average. In this case, we can see that these differences are high compared to the value considered. Similarly, there are very small differences or overlaps between the different average values. This complicates the classification phase.

Classes

350 351 352 353

PCA1 1155.99±5.13 1642.90±18.67 1402.45±15.46 1342.67±25.86 1304.05±15.59 1657.73±22.97 1453.31±25.06 1590.69±22.97 1512.48±11.91 1324.56±7.67 1853.33±4.62 1694.12±3.24 1837.82±6.56 1512.66±10.69 1506.06±5.18

A B C D E F G H I J K L M N O

Features PCA 2 376.39±5.69 169.71±13.93 460.36±8.88 107.49±14.34 153.23±8.88 359.52±12.67 268.39±11.06 76.27±5.73 239.84±9.54 83.80±12.22 74.74±7.35 212.24±11.57 463.67±12.61 455.31±14.21 337.11±10.90

A B C D E F G H I J K L M N O

ICA1 -0.502±1.760 0.469±1.584 0.370±1.536 -0.944±0.772 -1.036±0.234 0.982±0.205 0.963±0.816 0.991±0.339 -1.020±0.223 -0.499±1.642 0.477±1.724 -0.558±1.639 -0.072±1.261 0.930±0.396 -0.548±1.543

Features ICA2 -0.566±1.717 -0.007±1.284 -0.251±1.370 0.183±1.394 -0.009±1.297 -0.330±1.451 -0.144±1.371 -0.427±1.619 0.470±1.436 -1.033±0.414 0.516±1.776 0.006±1.265 0.595±1.650 1.030±0.293 -0.033±1.475

ICA3 -0.960±0.377 0.028±1.450 0.686±1.776 -0.429±1.605 -0.659±1.716 0.495±1.719 -0.681±1.732 -0.024±1.290 -0.444±1.579 -0.478±1.729 0.013±1.246 1.064±.1840 0.844±1.569 0.516±1.633 0.029±1.333 11

358 359 360 361 362 363

A B C D E F G H I J K L M N O

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Figure 9- ICA projection in two dimensions. Each of the points on this figure corresponds to the representation of the two characteristics obtained for a parasite image. We note that for the images of the same class, the points are widely dispersed. In this case, the network discrimination function of classification is difficult to obtain. -50

-100 A; B; C; D; E; F; G; H; I; J; K; L; M; N; O.

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Figure 10- PCA Projection in two dimensions. Each of the points on this figure corresponds to the representation of the two characteristics obtained from a parasite image. We note that, for images of the same class, the points are grouped separately from those of other classes. In this case, the network discrimination function of the classification is easy to obtain.

Thus, our choice was focused on principal components analysis. The first two principal components were used. We utilized a transformation

(f :

144

→

2

) of matrix W to obtain the values of the features in the new coordinate system.

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364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398

Figure 11-Microscopic images of each of the 15 types of parasites to recognize. A: Balantidium coli cyst; B: Endolimax nana cyst; C: Entamoeba coli cyst; D: Entamoeba hartmanni cyst; E: Entamoba histolytica cyst; F: Entamoeba polecki cyst; G: Giardia lamblia cyst; H: Iodamoeba butschlii cyst; I: Chilomastix mesnili cyst; J: Ascaris egg; K: Tapeworm egg; L: Schistosoma mansoni egg; M: Schistosoma Intercalatum egg; N: Schistosoma japonicum egg; O: whipworm egg.

399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415

We used 15 different species of intestinal parasites. Microscopic images of each of these parasites is given in Figure 11. In the list, there are 9 types of protozoan cysts: Giardia lamblia, Entamoeba hartmanni, Entamoeba polecki, Entamoeba histolytica, Entamoeba coli, Endolimax nana, Balantidium coli, chilomastix mesnili and Iodamoeba Butschlii. One can also distinguish 6 types of helminthes eggs: Ascaris, Tapeworm, Schistosoma mansoni, Schistosoma intercalatum, Schistosoma japonicum, and whipworm. All of these images were subjected to rotations of 0° to 150° at the step rate of 30°; we used five different scales. In addition, four types of noise were added separately to these images with an algebraic addition operation ("Gaussian noise", "Poisson noise", "salt & pepper noise", "speckle noise"). For each parasite type, we used 120 microscopic images, a database of 1800 images in total. For training the network, half randomly selected from this database was used. The other half of the database was used for testing; hence 60 images for each class of parasite. The training phase performed well -. Table 3 provides the test confusion matrix of our recognition system. According to this table, all of the fifteen types of parasites were classified with a 100% correct recognition rate. This shows the efficacy of the type of features descriptor used (PCA), as well as the probabilistic neural network in a recognition system of intestinal parasites. Indeed, the success rate can be predicated from the feature extraction results which previously regrouped the same classes and separated it from the others. When these classes are separated, it becomes easy to classify by the neural network insofar as the boundary of class separation will be well defined. As can be seen on the feature extraction results in Figure 10, the boundary of class separation is not linear. The success of the recognition phase is also justified by the fact that the probabilistic neural network proceeds by defining the center and the radius of each class in its radial input layer. Subsequently, its competitive output layer is responsible for directing the entrance to the class closest to it following this center and this radius.

3.4 Results of the parasite recognition

C

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B

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F

416 13

Table 3- Confusion matrix of the classification system: 60 samples of parasites per class; the test gives a rate of 100% correct recognition (60/60) for all of the 15 parasite classes considered.

Target classes

current classes

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419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442

4. Discussion In this paper, we proposed a method for automatic diagnose of intestinal parasites with the aid of digital image processing and pattern recognition. The process performs via the following steps: parasite detection, parasite segmentation and extraction, recognition of the parasite. We have performed and evaluated all steps in order to obtain the choice which contributes to the best diagnostic. The particularity of the microscopic images of stools is that it contains sufficient noise, and many other elements are not needed for parasite detection. For the first step, we need to detect the edge of the parasite. We used the multi-scale wavelet transform. In [16], it is shown that the multi-scale wavelet transform performs well for edge detection of intestinal parasites. The main advantage of this edge detection method is the scale of the analysis. As can be seen in figure 5, the image is analyzed on many scales and the retained result reveals the contours of the intestinal parasite and rejects the contours of undesirable elements. The second step uses the edge detection result to provide segmentation and extraction of the parasite. For this step, we need to extract individually each parasite and separate it from its background prior to recognition. We used the combination of the circular Hough transform and the active contours methods. The efficacy of this combination has been shown in [19]. The Hough transform method uses the image edge to reveal by vote the existing parametric forms in the image. Certain forms of intestinal parasites such as cysts are circular. Others are ovoid. For the first case, the circular Hough transform well detects the external contour of the parasite. For the ovoid forms of parasites and other closely related forms, the circular Hough transform partially locates the contour of the parasite. This partial contour serves as an initial contour for active contours models. We know that the success of the active contours method depends strongly on the initial contour. For our case, this initial contour is obtained automatically. With the external contour of the parasite, we just need to perform a logic operation to extract the parasite image and reject its background. As can be seen in Figure 6, our system performs automatically to find and extract a parasite in the microscopic images of stools. However, to facilitate searching, the dimension of the parasite can be provided to the system. For the last step, the extracted parasite is recognized via an artificial neural network. The dimension of the image parasite is sufficient to serve directly as the input features vector of the classification tool. We reduced this dimension by using principal components analysis (PCA). PCA extracts a features vector from the image of parasite. The PCA results are presented in Figure 9. In this figure, we can see that the same classes of parasites are grouped together, and different classes are separated. This is

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helpful because the separation curves of the classification tool can then be readily obtained. The artificial neural network (ANN) is a classification tool which imitates the activity of the neurons in the human brain. We choose the probabilistic neural network type for the simplicity of its structure and ease of the training phase. The results of the recognition step are presented in Table 3. The concerned parasites are showing in Figure 11. We used 15 types of intestinal parasites (9 amoeba cysts and 6 helminth eggs). We obtained a 100 % correct classification rate.

465

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5. Conclusions We presented in this paper a microscopic image processing system for medical diagnosis of intestinal parasites. This image processing technique includes the detection of the parasite, its extraction and recognition. The wavelet transform has allowed us to detect the parasite egg contours. The contours obtained were subsequently exploited, to segment the image and highlight the parasite. Our segmentation tool uses a snake technique initialized by the Hough transform. This hybrid method is confirmed to have high performance in the extraction of intestinal parasites. In addition, it allows automatic and individual extraction of the parasite prior to identification. The probabilistic neural network has proven to be suitable for the recognition of the parasites extracted. In addition to its simple structure, the training of the probabilistic neural network is fast. Unlike previous work, vectors of 12 × 12 size characteristic were obtained directly from the image pixels. These features have been projected in a basis using principal components analysis for the reduction of dimensionality. The reduced features vector served as input to the network. The results indicate that the feature vector, consisting of the gray levels of the image pixels, is achievable. In addition, this type of features provides for a remarkable performance when using a probabilistic neural network classifier, after reducing the dimensionality of the features by projection on a base of principal components analysis. Our system can automatically recognize an intestinal parasite through feces microscopic images loaded from a digital camera or scanner. We obtained some very interesting results. We believe that a significant step has been taken towards the development of a medical system for automatic diagnosis of human intestinal parasites.

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