ASSISTING HYBRIDIZED MICROSCOPIC IMAGING

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1 ASSISTING HYBRIDIZED MICROSCOPIC IMAGING ALEKSANDAR JOVANOVIĆ, MIROSLAV MARIĆ, MAJA JOVANOVIĆ, NENAD ANDONOVSKI GIS - Group for Intelligent Systems, School of Mathematics, University of Belgrade, Studentski trg Belgrade SERBIA AND MONTENEGRO Abstract: - Microscopic in situ hybridization ISH and Fluorescent in situ hybridization - FISH are well established methods of transformation of phenomena at single molecule level to the microscopic observable, employing molecular biology techniques for variety of applications including more precise diagnostics. Features of the imaging system which assists work with hybridized preparations in research and diagnostics at our institutes for molecular biology, oncology and pathology is here presented, namely: dot counting (ISH) and color fusions of FISH monochromes. Key-Words: - ISH, FISH, object extraction, dot counting, false-color fusions 1 Introduction Ten years ago meeting our neuromolecular biologists in one of their labs we learned that their graduate students were angaged in dot counting on hundreds of images like those below. The dark dots were in situ hybridized RNA molecules, which become visible, important are those within neural cell nuclei. We proposed to build a digital imagining system expanding their visual microscope and software which would assist humans and make their work easier and more efficient. After preliminary success, such systems were multiplied around existing optical microscopes and enriched with other functions. One of challenges was a need to enhance originally poor visual quality with reduced visibility present in fluorescent in situ hybridized material, which is exposed to fast erosion due to UV - high energy, thus strengthening the need to record all essential information in first preparation observations. That resulted in our color fusion tool which assists users very efficiently against mentioned obstacles. Fig. 1 Cancer cells, middle-identified contour, top designated spots

2 2 Method and Implementation 2.1 Dot-counting Basicaly, dot or spot counting in digital images (announced in [1] and [2]) is an elementary problem, reduced to the identification of pixels with locally maximal absorption, ie local minimums in representing matrix. Combined with contour detection and granular size selection it easily becomes hard to impossible for complete automatization. For this reason our implementation has both automatic and user controlled semi automatic solutions. Quite hard example is shown in Fig.1 with cancer cells. The top preparation exhibits a cell embedded in the environment in such way that makes an automatic cell extraction procedure very hard. After manual contour definition in this case, identification of dots is rather standard. We have implemented diverse techniques for automatic object extraction. For the need of further quantifications, like dot counting, volume and photo densitometric measurements more than object detection is needed: the best contour definition becomes crucial. Fig.2 shows dotted neural cell nuclei embedded in cytoplasmic material - environment, which is optically non homogenous. Some parts of central nucleus are thinner than inter nucleic medium, shown top left. Towards buttom right we have step-by-step reduction of inter nucleic background, leading to the nicely separated nuclei, determined by earlier defined average size of lower intensity objects. Before application of counting, there is a contour smoothing option which expands nucleus back to the desired contour smoothness level. Dot counting results depend on the size of granulae. In the Fig.3 and Fig.4 we have 132 dots of 11 pixel diameter, up to 793 dots of 3 pix in diameter. Granular size selection depends on the preparation production procedures and involved image magnification and needs to be calibrated. We suggest double verification by independent algorithms when statistics demands are narrow. With larger granulae our method reaches stability with respect to contour convexing steps (illustrated in Fig.5). This software has been in use in preparation of scientific results published by the quality journals (for example reference [5]) and used in experimental diagnostics. Fig. 2 Neural cell nuclei with ISH RNA molecules; object extraction step by step Fig. 3 Nucleus contour definition; dot counting: top 130 dot diameter =11 pix; middle 182 dot diameter=9 pix

3 2.2 False color fusions in FISH Fusion of color images from various sensors we applied earlier in astronomy ([3] and [2]), in order to visualize astrophysics of radio, x-ray, γ ray, IR, UV and visual sources. Our product was primarily aimed at CCD-microscopy, where it enabled user to combine CCD images obtained in all standard and nonstandard wavelengths in visual and UV - microscopy, combining disjoint techniques into easy and comfortable fusions, that allowed clinic and research to see invisible relations in the examined preparations, with user controls of all segments in production of final composites. Fig. 4 Counting continued: top 244 with dot diameter = 7 pix, middle 364 with dot diameter =5 pix and buttom count 793 with dot diameter = 3 pix Fig. 6 FISH input monochrome 1 Fig. 5 Counting stability versus contour reshaping: lower left original contour with 130 dots; top left: contour convexing (shown inverse) results in 10 more counts Fig. 7 FISH input monochrome 2

4 Fig. 8 FISH input monochrome 3 Fig. 11 Fusion of selected monochromes, after centering Here described software is still under development, expected to expand the number of features and to incorporate in future other specifics, especially virtual optical components - spectroscopic lenseing, based on our method of image spectroscopy, applied in the corrections of defects present in the microscopy at high magnifications. Suppose the recordings of microscopic data, originating in all perceptible windows, are prepared in the form of.bmp inputs, i.e. made available in some sort of standard visual form. Designate the mentioned windows as Fig. 9 Monochromes together W = {w s1,1 ;... w s1,n1 ; w s2,1 ;... ; w s2,n2 ;... ws k,1 ;... w sk,nk }, with source type domains {s 1, s 2,..., s k }. Allow the preprocessing operations on the separate domains that would provide for filtering, noise reduction, sharpening, some feature enhancing, centering (so that the contained objects are positioned at the same coordinates), aiming finally to the linear combinations that will integrate one out for each source type o i = j λ i w si,j, Fig. 10 Fusion of selected monochromes, before centering for i {1, 2,..., k}. So obtained type representatives are further individually and combined processed and centered before they are entered into final pre color monochrome fusion: m i = P j (o i1,..., o il ), i {1, 2, 3}.

5 Fig. 12 Color gallery : dominant G Fig. 13 Color gallery : dominant R somewhat simpler: in the Fig.6, Fig.7 and Fig.8 we have 3 FISH monochrome sources, together in the Fig.9. Entering color fusions in the Fig.10 (with no filtering), which shows the source aiming into R, G, B color components (arbitrary) and that fused images are not centered. That was due to physical operations on microscope - changes of monochrome filters. The recentering is done componentwise, using top arrows, with the pixel precision, all in less than a minute, if there are objects present in centering pairs (result of recentering illustrated in Fig.11). Color balance controls to the left enable user to create false color gallery that comprises all diversity in no time at all. This gallery provides for insight into transversal predicates that includes relations like multiple gene signals with different characteristic wavelengths, gene-chromosome, cariotyping, (at least partial), etc. In the Fig.12, Fig.13 and Fig. 14 we have such a false color gallery, the last images showing presence of 2 different FISH signals. This software is in use in our molecular biology, genetic, oncology and pathology labs. It can be downloaded from our web site References [1] A. Jovanović, Mathematics in biology, (Serb), School of Mathematics, University of Belgrade, [2] A. Jovanović, Group for Intelligent Systems - Problems and Results, (Russ) Intelektualnie sistemi, Lomonossov Un, tom 6, vip 1-4, Moscow, Fig. 14 Enhanced R and B, half reduced G, with both types of signals visible white and blue Both initial and final centering consist of combined translations, rotations and zooming. In such a way, comfortably and efficiently, a gallery of color composites cc j = (ρ j m 1, γ j m 2, β j m 3 ), j {1, 2,..., v}, is generated in real time, offering to the researchers potentially reach insight into the investigated phenomena. We illustrated the method [3] A. Jovanović, Z. Djordjević, F. Marić, M. Marić, D. Perišić, A toll for all astro sensor recordings fusion into color composite images, Serbian Astronomical Journal, [4] Group for intelligent systems - GIS, School of Mathematics, University of Belgrade, [5] S. Vukosavić, S. Ruždijić, R. Veskov, Lj. Rakić, S. Kanazir, Differential effects of amphetamine and phencyclidine on the expression of growthassociated protein GAP-43, Neuroscience Research 40, 2001.