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Image processing of rat hepatocytes
IMAGE PROCESSING
OF RAT HEPATOCYTES
G. ZAIICEK
Senior Lecturer in Experimental Medicine and Cancer Reseor& Head, Computer Unit, The Hebrew University--Hadapsoh Medical School, Jerusakm (Israel) (Received: 7 March, 1974)
SUMMARY
Rat liver histological sections were photographed through a Leitz microscope and the negative digitised using a P-1000 Photoscan micro-densitometer (OPTROhWX’). In the system the transmitted light through the filmwas measured using a photodetector, converted to 256 grey levels, and stored on a 7 track magnetic tape. The optical density was measured every 100 microns. The final picture consisted of 750,000 points. These were fed into a PDP-I 5140 computer with core requirements of 24 K words and two 250 K word disks, and analysed. The present paper describes a detailed analysis of one heptocyte.
SOMMAIRE
Des coupes histologiques d’un foie de rat ont ett! photographiees avec un microscope Leitz. L.es negatifs ont CtP digitalis& en utilisant un micro-densitometre d balayage P-1000 (OPTRONZCS). Dans ce systeme, la lumiere transmise h travers le filmest mesuree avec un photodetecteta, convertie sur 256 niveaux de gris, et stockee sur une ban& magnetique de 7 pistes. La Amsite optique est mesurb tous les 100 microns. La representation finale est faite de 750,000 points. Ceux-ci sont trait& sur un ordinateur PDP-15140 equipe dune m&moire centrale de 24 K mots et de deux disques de 250 K mots. Cet article decrit une analyse detaillee dun hepatocyte.
INTRODUCTION
Compute&d image processing of cells and tissues has been attempted by several authors. In a recent review Prewitt (1972) summarised the techniques of leukocyte 225 ht. J. Bio-Medical Computing (5) (1974)-o Printed in Great Britain
Applied, Science Publishers Ltd, England, 1974
226
G. ZAJICEK
image processing. Wied et al. (I 970), developed the TICAS System for automatic analysis of images. Rutovitz et al. (1970) developed a system for automatic chromosome analysis. The major difficulty encountered by all these authors was the amount of information generated even by the simplest digitising system, necessitating drastic data reduction and causing oversimplification of the processed image. Being interested mainly in histological patterns and relationships, we decided to store parts of histological sections in the computer and study data reduction methods preserving relations and patterns. The project under study was designed to meet the following objectives: (1) Reproduction of the original image or its part on the CRT screen. (2) Evaluation of density distributions of the picture or its part. (3) Development of algorithms to track contours of biologically relevant patterns in the section. (4) Quantitative derivation of biologically interesting parameters such as nuclear diameter distribution, nuclear cytoplasmic ratio, intra-nuclear chromatin distributions, etc. This paper describes work undertaken to meet the first objective.
MATERIAL AND METHOD
Rat liver histological sections were prepared by routine methods; fixed in Bouin and embedded in paraffin. The 6 micron sections were stained in Haematoxylin and Eosin and photographed through a Leitz microscope with a tungsten filament lamp illumination (12 V, 60 W). The film: Ektapan (9 cm x 12 cm, 100 ASA) was moderately fine grain with medium contrast. Exposures were made through a 58 filter. Light intensity was adjusted with the aid of a densitometer (Densichron) so as to fix the exposure time to six seconds. The final magnification of the image on the negative was 260. The scanning was performed by System P-1000 Photoscan micro-densitometer (OPTRONICS Internat. Inc., Chelmsford, Mass., USA), which consisted of an electro-optical rotating drum. The negative transparency was mounted on the drum. A Koehler illumination system ensured uniform illumination and focusing of turret-mounted apertures on the film surface. The transmitted light through the film was measured using a photodetector and converted to 256 grey levels. The receiving or imaging optical aperture was 100 micron square. The detector voltage resulting from the light transmitted through the film was amplified logarithmically, digitised and recorded on a seven track digital tape 800 bpc. The density range was set equivalent to an optical density of 2D. Optical density was defined as the log I,,/I, where 1, is the light intensity impinging on the detector through air path and I, is the light intensity of transmitted light. The
IMAGE PROCESSING
227
negative sire was 9 cm x 12 cm. Optical density of the tim along its Y axis was measured every 100 microns. After each drum revolution the imaging optics were stepped in the X direction 100 microns and the next Y axis was scanned. The process was repeated until the total area of interest was scanned. All Y axis points consisted of one record. The start and end of each record were marked on the tape. Each record consisted of an average of 750 points. Each picture consists of 1000 records. The digitised data were read from the magtape into a PDP-15/40 computer with core requirements of 24 K words and two 250 K word disks. RESULTS
Figure 1 shows an image_of a histologicai section which was digitised into 750,000 points. The hepatocyte in the upper right corner of the enclosure will be further described in detail. Its magnified image (Fig. 3) was photographed separately. The digit&d images (Figs. 2,4 and 5) are part of the original picture. In ordeito store the whole picture on disk, two neighbouring image points had to be packed in one computer word, reducing the necessary accessory storage to 375,000 words. The image was then projected on the CRT screen. Since the latter was restricted only to two shades of grey (0 and 1) and the image consists of 256 shades of grey, a density window through which the image was viewed on the CRT had to be defined.
Fig. 1.
The picture of the original image digitised into 750,000 points. The hepatocyte enclosure is depicted in .subsequent figures.
in the
228
G. ZAJICEK
Figure 2 shows the nuclear area of a hepatocyte comprising 3782 points viewed through a density window which spans between 95 and 145 relative density units. The undigitised image of this cell is depicted in Fig, 3 ; a further narrowing of the density window (Fig. 4) delineates the nuclear membrane. Cytoplasmic structures like vacuoles of lipid droplets lie below the relative density of 60. Their outlines are given in Fig. 5. All are clearly identifiable in the original. picture; however, due to photographic limitations, only the round vacuole in the right upper quadrant is also visible in Fig. 3. Thus the choice of the proper density window emphasises desired histological structures. This can be easily met by the study of the two-dimensional density distribution over the image (Fig. 6). Here the cell of Fig. 2 forms the.basis of the distribution and extends along the X and Y axes, while the point densities extend vertically in the 2 direction. The transition from cytoplasma to nucleus is associated here with a marked increase in relative density. The hill above the nucleus results from its being darker than the cytoplasm. A cross-section through this hill (Fig. 7) depicts valleys in the nucleus which are the brighter nucleoli. The latter constitute the holes in the nucleus of Fig. 2. The cytoplasmic valleys are parts of lipid vacuoles which are,the brightest structures in the image. When viewed through the’95-145 density window (Fig. 8) nuclear slopes are depicted.
Fig. 2.
One digit&d hepatocytc comprising 3782 points. The abscissa consists of 62 points and the ordinate of 61. Only points with relative densities of 95-145 are depicted.
IMAGE PROCESSING
229
Fig. 31. Higher magnification of the enclosed image of Fig. 1. The image is rotated clocks rise: by 90”. The digitised image of the bottom hepatocyte is depicted in subsequent figures.
Fig. 4.
‘Nuclear membrane’ comprising density points between 100410.
230
Fig. 5.
G. ZAJICEK
Cytoplasmic inclusions and outlines of vacuoles viewed through density windows
Of‘60 -80.
Fig. 6. Two-dimensional density distribution over the hepatocyte of Fig. 2. The cell is spread along the X, Y axes. The density value of each point extends towards the vertical-Z axis.
IMAGE PROCESSING
Fig. 7.
Fig. 8.
A cross-section
The density distribution the transition
through the density distribution
231
in the nuclear region.
viewed through a 95-145 density window. The slopes represent between nucleus and perinuclear cytoplasm.
232
G. ZAJICEK
DISCUSSION
Image processing consists of three steps: digitisation, data reduction and formulation of pattern algorithms. In our system digitisation yields reproducible images. The density distributions may differ quantitatively, but the density relations between structures in one image are preserved. Data reduction and the formulation of pattern algorithms depend on the structures in the image in which one is interested. At present our aim is to determine the nuclear area distributions in the original image. This is easily achieved by looking at the image through the 95-145 density window. Under such circumstances a formulation of a membrane tracking or nuclear tracking algorithms is relatively easy and will be presented in a subsequent publication.
REFERENCES P~~wrrr, J. M. S., Parametric and nonparametric recognition by computer. An application to leukocyte image processing, Adv. Computers, 12 (1972) p. 285. Rwrovrrz, D., FARROW,A. S. J., GREEN,D. K., HILD~I-I, C. J., PATON,K. A., and STEIN,B.. A system of automatic chromosome analysis. In: Medical computing @xi.) M. E. Abram% Chatto Windus, London, 1970, p. 35. WIEQ G. L., BAHR,G. F., and BARTEL~, P. H., Automatic cell analysis of cell images by TICAS, In: Automated cell identification and cell sorting (eds) G. L. Wied and G. F. Bahr, AC Press, New York, 1970, p. 195.
OF RAT HEPATOCYTES
G. ZAIICEK
Senior Lecturer in Experimental Medicine and Cancer Reseor& Head, Computer Unit, The Hebrew University--Hadapsoh Medical School, Jerusakm (Israel) (Received: 7 March, 1974)
SUMMARY
Rat liver histological sections were photographed through a Leitz microscope and the negative digitised using a P-1000 Photoscan micro-densitometer (OPTROhWX’). In the system the transmitted light through the filmwas measured using a photodetector, converted to 256 grey levels, and stored on a 7 track magnetic tape. The optical density was measured every 100 microns. The final picture consisted of 750,000 points. These were fed into a PDP-I 5140 computer with core requirements of 24 K words and two 250 K word disks, and analysed. The present paper describes a detailed analysis of one heptocyte.
SOMMAIRE
Des coupes histologiques d’un foie de rat ont ett! photographiees avec un microscope Leitz. L.es negatifs ont CtP digitalis& en utilisant un micro-densitometre d balayage P-1000 (OPTRONZCS). Dans ce systeme, la lumiere transmise h travers le filmest mesuree avec un photodetecteta, convertie sur 256 niveaux de gris, et stockee sur une ban& magnetique de 7 pistes. La Amsite optique est mesurb tous les 100 microns. La representation finale est faite de 750,000 points. Ceux-ci sont trait& sur un ordinateur PDP-15140 equipe dune m&moire centrale de 24 K mots et de deux disques de 250 K mots. Cet article decrit une analyse detaillee dun hepatocyte.
INTRODUCTION
Compute&d image processing of cells and tissues has been attempted by several authors. In a recent review Prewitt (1972) summarised the techniques of leukocyte 225 ht. J. Bio-Medical Computing (5) (1974)-o Printed in Great Britain
Applied, Science Publishers Ltd, England, 1974
226
G. ZAJICEK
image processing. Wied et al. (I 970), developed the TICAS System for automatic analysis of images. Rutovitz et al. (1970) developed a system for automatic chromosome analysis. The major difficulty encountered by all these authors was the amount of information generated even by the simplest digitising system, necessitating drastic data reduction and causing oversimplification of the processed image. Being interested mainly in histological patterns and relationships, we decided to store parts of histological sections in the computer and study data reduction methods preserving relations and patterns. The project under study was designed to meet the following objectives: (1) Reproduction of the original image or its part on the CRT screen. (2) Evaluation of density distributions of the picture or its part. (3) Development of algorithms to track contours of biologically relevant patterns in the section. (4) Quantitative derivation of biologically interesting parameters such as nuclear diameter distribution, nuclear cytoplasmic ratio, intra-nuclear chromatin distributions, etc. This paper describes work undertaken to meet the first objective.
MATERIAL AND METHOD
Rat liver histological sections were prepared by routine methods; fixed in Bouin and embedded in paraffin. The 6 micron sections were stained in Haematoxylin and Eosin and photographed through a Leitz microscope with a tungsten filament lamp illumination (12 V, 60 W). The film: Ektapan (9 cm x 12 cm, 100 ASA) was moderately fine grain with medium contrast. Exposures were made through a 58 filter. Light intensity was adjusted with the aid of a densitometer (Densichron) so as to fix the exposure time to six seconds. The final magnification of the image on the negative was 260. The scanning was performed by System P-1000 Photoscan micro-densitometer (OPTRONICS Internat. Inc., Chelmsford, Mass., USA), which consisted of an electro-optical rotating drum. The negative transparency was mounted on the drum. A Koehler illumination system ensured uniform illumination and focusing of turret-mounted apertures on the film surface. The transmitted light through the film was measured using a photodetector and converted to 256 grey levels. The receiving or imaging optical aperture was 100 micron square. The detector voltage resulting from the light transmitted through the film was amplified logarithmically, digitised and recorded on a seven track digital tape 800 bpc. The density range was set equivalent to an optical density of 2D. Optical density was defined as the log I,,/I, where 1, is the light intensity impinging on the detector through air path and I, is the light intensity of transmitted light. The
IMAGE PROCESSING
227
negative sire was 9 cm x 12 cm. Optical density of the tim along its Y axis was measured every 100 microns. After each drum revolution the imaging optics were stepped in the X direction 100 microns and the next Y axis was scanned. The process was repeated until the total area of interest was scanned. All Y axis points consisted of one record. The start and end of each record were marked on the tape. Each record consisted of an average of 750 points. Each picture consists of 1000 records. The digitised data were read from the magtape into a PDP-15/40 computer with core requirements of 24 K words and two 250 K word disks. RESULTS
Figure 1 shows an image_of a histologicai section which was digitised into 750,000 points. The hepatocyte in the upper right corner of the enclosure will be further described in detail. Its magnified image (Fig. 3) was photographed separately. The digit&d images (Figs. 2,4 and 5) are part of the original picture. In ordeito store the whole picture on disk, two neighbouring image points had to be packed in one computer word, reducing the necessary accessory storage to 375,000 words. The image was then projected on the CRT screen. Since the latter was restricted only to two shades of grey (0 and 1) and the image consists of 256 shades of grey, a density window through which the image was viewed on the CRT had to be defined.
Fig. 1.
The picture of the original image digitised into 750,000 points. The hepatocyte enclosure is depicted in .subsequent figures.
in the
228
G. ZAJICEK
Figure 2 shows the nuclear area of a hepatocyte comprising 3782 points viewed through a density window which spans between 95 and 145 relative density units. The undigitised image of this cell is depicted in Fig, 3 ; a further narrowing of the density window (Fig. 4) delineates the nuclear membrane. Cytoplasmic structures like vacuoles of lipid droplets lie below the relative density of 60. Their outlines are given in Fig. 5. All are clearly identifiable in the original. picture; however, due to photographic limitations, only the round vacuole in the right upper quadrant is also visible in Fig. 3. Thus the choice of the proper density window emphasises desired histological structures. This can be easily met by the study of the two-dimensional density distribution over the image (Fig. 6). Here the cell of Fig. 2 forms the.basis of the distribution and extends along the X and Y axes, while the point densities extend vertically in the 2 direction. The transition from cytoplasma to nucleus is associated here with a marked increase in relative density. The hill above the nucleus results from its being darker than the cytoplasm. A cross-section through this hill (Fig. 7) depicts valleys in the nucleus which are the brighter nucleoli. The latter constitute the holes in the nucleus of Fig. 2. The cytoplasmic valleys are parts of lipid vacuoles which are,the brightest structures in the image. When viewed through the’95-145 density window (Fig. 8) nuclear slopes are depicted.
Fig. 2.
One digit&d hepatocytc comprising 3782 points. The abscissa consists of 62 points and the ordinate of 61. Only points with relative densities of 95-145 are depicted.
IMAGE PROCESSING
229
Fig. 31. Higher magnification of the enclosed image of Fig. 1. The image is rotated clocks rise: by 90”. The digitised image of the bottom hepatocyte is depicted in subsequent figures.
Fig. 4.
‘Nuclear membrane’ comprising density points between 100410.
230
Fig. 5.
G. ZAJICEK
Cytoplasmic inclusions and outlines of vacuoles viewed through density windows
Of‘60 -80.
Fig. 6. Two-dimensional density distribution over the hepatocyte of Fig. 2. The cell is spread along the X, Y axes. The density value of each point extends towards the vertical-Z axis.
IMAGE PROCESSING
Fig. 7.
Fig. 8.
A cross-section
The density distribution the transition
through the density distribution
231
in the nuclear region.
viewed through a 95-145 density window. The slopes represent between nucleus and perinuclear cytoplasm.
232
G. ZAJICEK
DISCUSSION
Image processing consists of three steps: digitisation, data reduction and formulation of pattern algorithms. In our system digitisation yields reproducible images. The density distributions may differ quantitatively, but the density relations between structures in one image are preserved. Data reduction and the formulation of pattern algorithms depend on the structures in the image in which one is interested. At present our aim is to determine the nuclear area distributions in the original image. This is easily achieved by looking at the image through the 95-145 density window. Under such circumstances a formulation of a membrane tracking or nuclear tracking algorithms is relatively easy and will be presented in a subsequent publication.
REFERENCES P~~wrrr, J. M. S., Parametric and nonparametric recognition by computer. An application to leukocyte image processing, Adv. Computers, 12 (1972) p. 285. Rwrovrrz, D., FARROW,A. S. J., GREEN,D. K., HILD~I-I, C. J., PATON,K. A., and STEIN,B.. A system of automatic chromosome analysis. In: Medical computing @xi.) M. E. Abram% Chatto Windus, London, 1970, p. 35. WIEQ G. L., BAHR,G. F., and BARTEL~, P. H., Automatic cell analysis of cell images by TICAS, In: Automated cell identification and cell sorting (eds) G. L. Wied and G. F. Bahr, AC Press, New York, 1970, p. 195.