Digital pathology and microscopic image analysis play an important role in cell morphology research. In particular, the effective segmentation of White Blood Cells (WBCs) remains a challenging problem due to the blurring boundaries of WBCs under rapid staining, as well as the adhesion between leukocytes and other cells. In this paper, we propose a novel WBC (including nuclei and cells) segmentation algorithm based on both sparsity and geometry constraints. Specifically, we first construct a sparse image representation via combining the HSL color space and the RGB color channels, followed by the use of a sparsity constraint to only preserve useful information from the nuclei features. In addition, we introduce a robust model fitting strategy (i.e., the geometry constraint) to detect cells. Our model fitting strategy is able to significantly improve the robustness of the proposed segmentation algorithm against outliers that could seriously contaminate WBCs. The experimental results show that the proposed algorithm presents clear advantages over the state-of-the-art WBC segmentation algorithms in terms of accuracy.

Introduction

White blood cells (WBCs) [1], [2] are important defense cells in human blood that consists of five kinds of cells, i.e., neutrophils, basophils, eosinophils, monocytes, and lymphocytes. The WBC segmentation is a challenging task for a variety of medical diagnosis applications. For example, the visual examination of WBCs in blood smears collected under a bright field microscope can be used to diagnose various diseases, such as septic bacterial inflammation, uremia, and various kinds of leukaemia. A number of WBC segmentation methods have been proposed in recent years. In general, existing methods can be divided into two distinct categories: supervised vs. unsupervised WBC segmentation methods. The supervised WBC segmentation methods [3]–[6] formulate the WBC segmentation problem as a multi-class classification problem. They are typically solved in a two-step manner, i.e., first extracting image features and then classifying the extracted features. However, these supervised methods require a large number of annotated training samples, which are often manually labeled and difficult to obtain. Particularly, the manual pixelwise segmentation process is tedious and error-prone for the abundant fine structures in the blood cell imagery. In addition, the training and test images are assumed to be visually similar to minimize the domain shift between training and test images. In practice, this assumption could negatively impact the generalization abilities of these supervised algorithms.

Eye and systemic diseases are known to manifest themselves in retinal vasculature. Segmentation of retinal vessel is one of the important steps in retinal image analysis. A simple unsupervised method based on Gabor wavelet and Multiscale Line Detector is proposed for retinal vessel segmentation. Vessels are enhanced by linear superposition of first scale Gabor wavelet image and complemented Green channel. Multiscale Line Detector is used to segment the blood vessels. Finally, a simple post processing scheme based on median filtering is deployed to remove false positives. The proposed scheme was evaluated with publicly available datasets called DRIVE, STARE and HRF, obtaining an accuracy of 0.9470, 0.9472, and 0.9559, and a sensitivity of 0.7421, 0.8004, and 0.7207, respectively. These results are comparable to the state-of-the-art methods, albeit with a simpler approach.

Introduction

One of the important tasks in diagnosing different medical conditions such as diabetic retinopathy, cardiovascular diseases, and stroke is the segmentation of blood vessels in color medical images. To this end, different strategies have been devised. The strategies can be roughly grouped into i) multiscale, ii) matched filtering, iii) mathematical morphology, iv) hierarchical, v) model and vi) deep learning approach [1]. Furthermore, they can also be categorized into supervised and unsupervised algorithm. The prominent strategies based on multiscale are [2], [3]. Soares et al. [2] used Gabor wavelet transform with four scales (2,3,4,5) to account for different width sizes of blood vessel, and supervised classification. Nyugen et al. [4] proposed blood vessel segmentation using a multi-scale line detection based technique. The approach is an extension of the scheme based on single scale line detector and support vector machine [5]. Examples of filter based approach are [6]–[8] and mathematical morphology based are [9]–[11]. Retinal vessel segmentations based on hierarchical detections are [12], [13], model based approaches [14]–[16], and deep learning based schemes [13], [17]–[21]. One major challenge in retinal image analysis especially for accurate vessel detection is low and varying contrast. A method based on Gabor wavelet and multi-scale line detector is being proposed here. The Gabor wavelet transform presents high frequency precision in low frequencies and high spatial precision in high frequencies. In other words, the transform is suitable for detecting edges and other singularities in the image [22], [23].

مقاله سال 2017

International Journal of Advanced Research in Computer and Communication Engineering

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