In hematological morphology, it is necessary to resolve and analyze the smallest possible cellular details appearing in the light microscope. A prerequisite for computer-aided analysis of subtle morphological features is measuring the cells at a high scanning density with high magnification and high numerical aperture optics. Contrary to visual observations, the information content in a measured picture can be increased by setting the condensor's numerical aperture (NA) greater than the objective's NA. The complexity and heterogeneity of such cell images necessitate a new segmentation method that conserves the morphological information required in the subsequent image analysis, feature extraction, and cell classification. In our segmentation strategy, characteristic color difference thresholds for each nucleus and Multispectral and geometric segmentation algorithms have been documented by Young (27)' Brenner (6), Lemkin (161, Preston (22,231, Bacus (5) and Aus (4). The most common image segmentation methods are either gradient algorithms with skeleton and contour following operations (6,10,11,15,17) or clustering, erosion, and dilation processes (18-20). These methods typically operate at or near the boundaries in an object and have been mostly applied to images scanned at a low optical resolution and a scanning density of not more than 5 pixels per micrometer. High-resolution image acquisition requires at least 10 pixels per micrometer (12,14). The low scanning densities are advantageous in the digital segmentation process because the cellular regions are more homogeneous and the contrast between the various cellular regions is higher and easier to detect. Cell images digitized at such low scanning densities, however, contain less structural detail than a hemotologist needs to see in a high-resolution light microscope for his diagnocytoplasm are combined with geometric operations, probability functions, and a cell model. All thresholds are repeatedly recalculated during the successive improvements of the image masks. None of the thresholds are fixed. This strategy segments blood cell images containing touching cells and large variations in staining, texture, size, and shape. Biological inconsistencies in the calculated cell masks are eliminated by comparing each mask with the cell model criteria integrated into the entire segmentation process. All 20,000 leukocyte images from 120 smeam in our leukemia project were segmented with this method.Key terms: High-resolution image acquisition, segmentation directed by a cell model, color difference, geometric operations -~ sis. Many of his clinical applications require analyzing the smallest possible structures in cell images. These images must first be segmented into their major cellular components. This required new segmentation algorithms.In a light microscope, image quality is always a compromise between stray light, image contrast, and resolving the smallest possible detail in an object. The visible detail in an image depends, among other things, on the objective...
This study of 14 follicular adenomas, 10 papillary carcinomas and 11 follicular carcinomas of the human thyroid gland demonstrates the possibility of a cytological tumor classification using digital picture processing. Routinely prepared, HE-stained imprints of surgical specimens were scanned under a light microscope at high resolution with a colour TV camera. The cell nuclei were segmented and analysed with an image processing system. The computer-aided cytophotometric methods detected the most significant differences in the chromatin texture with a criteria variance of texture line distances and texture points per texture knots. Using these criteria benign and malignant tumor types could be successfully differentiated.
This report outlines the morphologic classification of acute myeloid (AML: French-American-British FAB classification: Ml) and lymphoid (ALL) leukemia by automatic image analysis and the correlation to immunologic and cytochemical classification. The investigation was carried out on Romanowsky-Giemsa stained bone marrow (n = 15) and blood smears (n -10) from 25 patients with primary acute leukemia. The cases had been classified as of myeloid or lymphoid origin by three hcmatologic centers using imtnunochemistry or cytochemistry, but the specimens were submitted to the authors' laboratory without the diagnosis. The nuclear and cytoplasmic pattern of the blast cells were analyzed by a high resolution image analysis system and the measured and The FAB classification has established a widely accepted morphologic categorization of acute leukemias with improved reproducibility and standardization.1 Major problems in applying morphologic classifications arise from the subjective impression of the observer and thus only a 60% to 80% degree of reproducibility. 23 However, morphologic cell classification based on high resolution computer aided image analysis is independent from subjective impression. 4 Therefore, the resulting cell analysis is based on objective and reproducible measurable criteria. This is primarily done by morphology, immunophenotype, and cytochemistry and additionally by cytogenetics and molecular genetics.The subtypes of acute myeloid leukemias (AML) are mainly diagnosed by morphologic and cytochemical features according to the FAB classification. 1710 Immunophenotyping appears to be indispensable in distinguishing the AML subtypes M0 and Ml with minimal signs of cellular maturation from acute lymphoid leukemia (ALL). Apart from the identification of promyelocytic leukemia FAB M3, neither morphologic nor immunologic classification of AML proved to be of prognostic relevance.10 However, the subtypes of ALL are distinguished predominantly by means of their immunophenotype, which proved to be of prognostic relevance, 6 " whereas morphologic assessment plays a minor role, apart from the subtypes with FAB L2 morphology. 1213In this study, we present a morphologic characterization of AML (FAB: Ml) and ALL by image analysis and 23 Downloaded from https://academic.oup.
The differentiation of the thyroid glands follicular neoplasias into adenomas and carcinomas is currently done using the histological criteria recommended by WHO. This pilot study of 10 human follicular carcinomas and 10 folliculars adenomas demonstrates the possibility of a cytological classification using digital picture processing of high resolution cell images. Giemsa stained paraplast sections were scanned with a Colour-TV-camera, different channels were used with respect to staining and analyzing methods and computed with an image processing system. The computer aided cytophotometric methods detected significant differences in the chromatin arrangement and structure.
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