Pathological image analysis plays a significant role in effective disease diagnostics. In this article, a tool for diagnosis assistance by automatic segmentation of bone marrow images is introduced. The aim of our segmentation is to demarcate cell's component: nucleus, cytoplasm, red cells, and background. Different color spaces were used to extract color's features to profit of their complementarity. We introduce several dimensionality reduction techniques. These techniques are exemplified on a support vector machine pixel‐based bone marrow image segmentation problem in which it is shown that it may give significant improvement in segmentation accuracy and time consuming. © 2013 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 23, 22–28, 2013

Deep learning–based medical image segmentation is henceforth widely established as a powerful segmentation process. This article proposes a new U‐Net architecture based on a convolutional neural network for cytology image segmentation. This structure is more suitable to take into account pixel neighborhood in deconvolution. The goal is to develop an accurate segmentation method for white blood cells segmentation based on cells types features. This new proposed method yields a significant improvement compared to our previous work on the cytological medical dataset. In addition, the performance of the new architecture was also successfully tested on the Digital Retinal Image for Vessel Extraction databases benchmark. The images of this challenge are similar to our cytology image segmentation. Our approach achieved 25% relative improvement of the accuracy compared to the state‐of‐the‐art.

In this paper, we propose a complete automated framework for white blood cells differential count in peripheral blood and bone marrow images, in order to reduce the analysis time and increase the accuracy of several blood disorders diagnosis. A new colour transformation is first proposed to highlight the white blood cells regions; then, a marker controlled watershed algorithm is used to segment the region of interest. The nucleus and cytoplasm are subsequently separated. In the identification step, a set of colour, texture and morphological features are extracted from both nucleus and cytoplasm regions. Next, the performances of a random forest classifier on a set of microscopic images are compared and evaluated. The obtained results reveal high recognition accuracies for both segmentation and classification stage.

The classification and the count of different White Blood Cells (WBC) in microscopy images allow the assessment of a wide range of important hematology pathologies such Myeloma. This article aims particularly myeloma disease; this pathology is manifested by a proliferation of a type of cell called plasma cells. This paper presents a robust and accurate novel method for processing WBC(leukocyte) using a combination of ideas. The segmentation of cells is achieved in two phases, the first is to extract the nucleus from the green channel of the image using the otsu's thresholding method, and the second operates through a region growing on the nucleus in order to delimitate a cytoplasm. After that, a cell's characterization step is performed where desired features can be extracted in order to classify types of leukocytes and thus allow the counting of plasma cells. The proposed process have been successfully tested to a large number of images, showing promising results in terms of segmentation and classification of plasma cells, and also applied to varying cell appearance and image quality, encouraging future works.

No abstract