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Finding leukocyte region in microscopic images
Abstract
Microscopic analysis of images in the health care at the cellular level is one of the important methods for analysis and final diagnosis. In the diagnosis of blood diseases, although high technology systems provide very important information, for a definitive diagnosis microscopic smear examinations are needed. Microscopic examination is a time-consuming task for doctors. Therefore, in this study a basic system has been developed that may speed up the eye examination. In future, further development of this system may be an alternative to visual examination. From the basic blood cells (leukocyte, erythrocyte, platelet), we only focused on the locations of white blood cells in the image. In the development process of this system real blood smear images has been used. In this study iterative algorithms are not used instead of this, logical and morphological processes have been used. This allows faster operation of the system.
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Aggregation of red blood cells which manifests in the form of rouleaux is a major determinant of flow behavior of blood in both micro and macro circulation. Any alteration in the normal behavior is found to be a critical factor in plugging of arterioles and venules forming irreversible clumps. In this work, the aggregation behavior of red blood cells is analyzed using Wavelet transforms. The artificially induced red cell aggregate images obtained from normal adult volunteers are used for the study. An arbitrary aggregation size index is derived using four different wavelet functions. The results demonstrate the ability of wavelets to precisely differentiate variations in degree of aggregation and their intercellular bonding. Also the aggregation size indices are found to be similar and consistent for a given aggregate for all wavelet functions. As the association and dissociation of red cells and the bonding strength during cluster formation are direct indicators of altered flow behavior of red cells in micro-vessels and large arteries such analysis seems to be clinically relevant. The methodology, algorithm and observations based on Wavelet transforms are discussed in detail.
In this paper, we propose a segmentation method for an automated differential counter using image analysis. The segmentation
here is to extract leukocytes (white blood cells) and separate its constituents, nucleus and cytoplasm, in blood smear images.
For this purpose, a region-based active contour model is used where region information is estimated using a statistical analysis.
The role of the regional statistics is mainly to attract evolving contours toward the boundaries of leukocytes, avoiding problems
with initialization. And contour deformation near to the boundaries is constrained by an additional regularizer. The active
contour model is implemented using a level set method and validated with a leukocyte image database.
An automatic segmentation technique for microscopic bone marrow white blood cell images is proposed in this paper. The segmentation
technique segments each cell image into three regions, i.e., nucleus, cytoplasm, and background. We evaluate the segmentation
performance of the proposed technique by comparing its results with the cell images manually segmented by an expert. The probability
of error in image segmentation is utilized as an evaluation measure in the comparison. From the experiments, we achieve good
segmentation performances in the entire cell and nucleus segmentation. The six-class cell classification problem is also investigated
by using the automatic segmented images. We extract four features from the segmented images including the cell area, the peak
location of pattern spectrum, the first and second granulometric moments of nucleus. Even though the boundaries between cell
classes are not well-defined and there are classification variations among experts, we achieve a promising classification
performance using neural networks with five-fold cross validation.
While the early diagnosis of hematopoietic system disorders is very important in hematology, it is a highly complex and time consuming task. The early diagnosis requires a lot of patients to be followed-up by experts which, in general is unfeasible because of the required number of experts. The differential blood counter (DBC) system that we have developed is an attempt to automate the task performed manually by experts in routine. In our system, the cells are segmented using active contour models (snakes and balloons), which are initialized using morphological operators. Shape based and texture based features are utilized for the classification task. Different classifiers such as k-nearest neighbors, learning vector quantization, multi-layer perceptron and support vector machine are employed.