Segmentation of Sinusoids in Hematoxylin and Eosin Stained Liver Specimens Using an Orientation-Selective Filter

Abstract

The liver comprises cell layers of hepatocytes called trabeculae, which are separated by vascular sinusoids. Understanding the structure of hepatic trabeculae and liver sinusoids in hematoxylin and eosin (HE)-stained liver specimens is important for the differential diagnosis of liver diseases. In this study, we develop an approach to extracting liver sinusoids from HE-stained images. The proposed approach involves: 1) a new orientation-selective filter (OS filter) for edge enhancement and image denoising, 2) the clustering of image pixels to identify candidate sinusoids, and 3) a classification procedure that discards unlikely candidates and selects the final sinusoid areas. Experimental studies using a database of 16 images with a resolution of 512 × 512 pixels showed that the proposed approach could segment liver sinusoid pixels with 81% of specificity and 94% of sensitivity. A comparison with a method based on bilateral filters showed that this method improved the sensitivity for all images with an average improvement of 4% and no difference in specificity. The results were presented to a group of pathologists and they confirmed that the images were highly representative of the tissue morphology features.

Severity Analysis of Cervical Cancer in Pap Smear Images by using EEETCM, ERSTCM & CFE method based Texture Features and Hybrid Kernel based Support Vector Machine Classifier.

“…In order to detect cervical cancer automatically with the help of screening tests. Among them one of the most important method is the segmentation of cell nuclei from the stained specimens [14]. However, isolated nuclei of the cells in high-quality acquisitions provide difficult tricks in the segmentation is more number of nuclei with various characteristics under occurring different acquisition conditions in good-resolution scans of the complete microscope slide [15].…”

Automatic quantification of morphological features for hepatic trabeculae analysis in stained liver specimens

“…We then apply the expectation-maximization (EM) algorithm to cluster bright pixels. filter is shown in Ishikawa et al, 15 where details of this step can be found.…”

“…Figure 3 shows an overview of the proposed method for the extraction and quantification of trabecular structure. We previously reported that trabeculae can be extracted using the structures outside liver cells (sinusoids and stromata), 14,15 which can be extracted more easily than liver cells themselves. The method introduced in this paper also follows the same approach: the sinusoid and stroma regions are segmented first, and then we extract the trabeculae regions by excluding those regions.…”

“…3 is composed of two main steps: trabecular segmentation and trabecular feature measurement. This section presents the trabecular segmentation steps: stroma and sinusoid extraction 14,15 are presented in Sec. 3.1, the methods for extracting of fat droplets are described in Sec.…”

“…The fat droplet extraction was carried out by a method proposed for fatty metamorphosis quantification in Ref. 15. Fat droplet extraction consists of four steps: (1) smoothing of the fat droplet boundaries using an OS filter, (2) extraction of candidate fat droplet regions, (3) fat droplet discrimination, and (4) modification of the trabeculae extraction results using fat droplet regions.…”