Computer-aided acute leukemia blast cells segmentation in peripheral blood images

Computer-aided diagnosis system of leukemic cells is vital tool, which can assist domain experts in the diagnosis and evaluation procedure. Accurate blast cells segmentation is the initial stage in building a successful computer-aided diagnosis system. Blast cells segmentation is still an open research topic due to several problems such as variation of blats cells in terms of color, shape and texture, touching and overlapping of cells, inconsistent image quality, etc. Although numerous blast cells segmentation methods have been developed, only few studies attempted to address these problems simultaneously. This paper presents a new image segmentation method to extract acute leukemia blast cells in peripheral blood. The first aim is to segment the leukemic cells by mean of color transformation and mathematical morphology. The method also introduces an approach to split overlapping cells using the marker-controlled watershed algorithm based on a new marker selection scheme. Furthermore, the paper presents a powerful approach to separate the nucleus region and the cytoplasm region based on the seeded region growing algorithm powered by histogram equalization and arithmetic addition to handle the issue of non-homogenous nuclear chromatin pattern. The robustness of the proposed method is tested on two datasets comprise of 1024 peripheral blood images acquired from two different medical centers. The quantitative evaluation reveals that the proposed method obtain a better segmentation performance compared with its counterparts and achieves remarkable segmentation results of approximately 96 % in blast cell extraction and 94 % in nucleus/cytoplasm separation

Liver Isolation in Abdominal MRI

[1] Cloud fields retrieved from remotely sensed satellite data resemble functions depicting spectral values at each spatial position (x, y). Segmenting such cloud fields through a simple thresholding technique may not provide any structurally significant information about each segmented category. An approach based on the use of multiscale convexity analysis to derive structurally significant regions from cloud fields is addressed in this paper. This analysis requires (1) the generation of cloud fields at coarser resolutions and (2) the construction of convex hulls of cloud fields, at corresponding resolutions by employing multiscale morphologic opening transformation and half-plane closings with certain logical operations. The three basic parameters required from these generated multiscale phenomena in order to accomplish the structure-based segmentation include (1) the areas of multiscale cloud fields, (2) the areas of corresponding convex hulls, and (3) the estimation of convexity measures at corresponding resolutions by employing the areas of cloud fields and areas of corresponding convex hulls. These convexity measures computed for multiscale cloud fields are plotted as a function of the resolution imposed owing to multiscale opening to derive a causal relationship. The scaling exponents derived from these graphical plots are taken as the basis for (1) determining the transition zones between the regimes and (2) segmenting the cloud fields into morphologically significant regions. We demonstrated this approach on two different cloud fields retrieved from Moderate Resolution Imaging Spectroradiometer (MODIS) data. The segmented regions from these cloud fields possess different degrees of spatial complexities. As many macroscale and microscale atmospheric fields are classified according to spatial variability indexes, the framework proposed here would supplement those existing atmospheric field classification methodologies