Automated Multi-Stage Segmentation of White Blood Cells Via Optimizing Color Processing

Segmentation of white blood cells (i.e. leukocytes) is a crucial step toward the development of haematological images analysis of peripheral blood smears due to the complex nature of the different types of white blood cells and their large variations in shape, texture, color, and density. This study addresses this issue and presents a single fully automatic segmentation framework for both nuclei and cytoplasm of the five classes of leukocytes in a microscope blood smears. The proposed framework integrates a priori information of enhanced nuclei color with Gram-Schmidt orthogonalization, and multi-scale morphological enhancement to localize the nuclei, whereas clustering-based seed extraction and watershed are utilized to segment the cytoplasm. The experimental results on two different datasets show that the proposed method works successfully for both nuclei and cytoplasm segmentation, and achieves more accurate segmentation results compared to the other methods in the literature

Automatic segmentation of overlapping cervical smear cells based on local distinctive features and guided shape deformation

Automated segmentation of cells from cervical smears poses great challenge to biomedical image analysis because of the noisy and complex background, poor cytoplasmic contrast and the presence of fuzzy and overlapping cells. In this paper, we propose an automated segmentation method for the nucleus and cytoplasm in a cluster of cervical cells based on distinctive local features and guided sparse shape deformation. Our proposed approach is performed in two stages: segmentation of nuclei and cellular clusters, and segmentation of overlapping cytoplasm. In the rst stage, a set of local discriminative shape and appearance cues of image superpixels is incorporated and classi ed by the Support Vector Machine (SVM) to segment the image into nuclei, cellular clusters, and background. In the second stage, a robust shape deformation framework is proposed, based on Sparse Coding (SC) theory and guided by representative shape features, to construct the cytoplasmic shape of each overlapping cell. Then, the obtained shape is re ned by the Distance Regularized Level Set Evolution (DRLSE) model. We evaluated our approach using the ISBI 2014 challenge dataset, which has 135 synthetic cell images for a total of 810 cells. Our results show that our approach outperformed existing approaches in segmenting overlapping cells and obtaining accurate nuclear boundaries. Keywords: overlapping cervical smear cells, feature extraction, sparse coding, shape deformation, distance regularized level set

Automated Segmentation of Touching and Overlapping Cells in Microscopic Images

This thesis presents numerous automated methods nuclei and cytoplasm segmentation from touching and overlapping cells in two challenging microscopic images, i.e., blood and Pap images. Particularly, we started our research work with segmentation of nuclei and cytoplasm of touching white blood cells based on colour and texture information (Chapter 3) and colour prior knowledge with Gram-Schmidt orthogonalisation (Chapter 4). In our next contributions, we moved toward the segmentation of overlapping cells which are typically present in Pap smear images. Chapter 5 incorporates gradient-based edge and region information to model the cytoplasm shape in partially overlapping cells. Despite its promising performance, it could not obtain the gradient information for complex cell mass with a non-homogeneous appearance. Thus, we adopt several shape information in our next contributions to enable accurate segmentation of complex overlapping cells. Specifically, in Chapter 6, we introduce a coarse-to-fine method to approximate the shape of partially overlapping cells based on training samples. Chapter 7 introduces a new method for highly overlapping cells with up to 0.5 overlapping ratios. Specifically, an adaptive initialisation procedure is designed based on edge and region information to guide the shape deformation toward the target cell shape. Chapter 8 introduces a novel shape-driven variational technique based on machine learning and a dynamic shape prior. Finally, Chapter 9 aims to capture the intricacies of the segmentation problem, including segmentation of multi-layer cytology volumes. This is done by establishing a cost-effective multi-pass watershed smartly controlled by cell position and shape information. All the proposed methods presented in this thesis are evaluated on publicly available databases and have demonstrated good performance compared to the state-of-the-art methods