Cytology, a method of estimating cancer or cellular atypia from microscopic images of scraped specimens, is used according to the pathologist’s experience to diagnose cases based on the degree of structural changes and atypia. Several methods of cell feature quantification, including nuclear size, nuclear shape, cytoplasm size, and chromatin texture, have been studied. We focus on chromatin distribution in the cell nucleus and propose new feature values that indicate the chromatin complexity, spreading, and bias, including convex hull ratio on multiple binary images, intensity distribution from the gravity center, and tangential component intensity and texture biases. The characteristics and cellular classification accuracies of the proposed features were verified through experiments using cervical smear samples, for which clear nuclear morphologic diagnostic criteria are available. In this experiment, we also used a stepwise support vector machine to create a machine learning model and a cross-validation algorithm with which to derive identification accuracy. Our results demonstrate the effectiveness of our proposed feature values.
In digital pathology diagnosis, accurate recognition and quantification of the tissue structure is an important factor for
computer-aided diagnosis. However, the classification accuracy of cytoplasm is low in Hematoxylin and eosin (HE) stained
liver pathology specimens because the RGB color values of cytoplasm are almost similar to that of fibers. In this paper,
we propose a new tissue classification method for HE stained liver pathology specimens by using hyperspectral image. At
first we select valid spectra from the image to make a clear distinction between fibers and cytoplasm, and then classify
five types of tissue based on the bag of features (BoF). The average classification accuracy for all tissues was improved
by 11% in the case of using BoF of RGB and selected spectra bands in comparison with using only RGB. In particular,
the improvement reached to 24% for fibers and 5% for cytoplasm.
The pathological diagnosis of a transplanted kidney is made on Banff Classification in order to gain an accurate
understanding of the condition of the kidney. This type of diagnosis is extremely difficult and, thus, a variety of methods
for diagnosis, including diagnosis by electron microscope, are being considered at present. Quantification of the
diagnostic information derived by image processing is required for such purposes. This study proposes an automatic
extraction method for normal glomeruli for the purpose of quantifying Elastica Van Gieson(EVG)-stained pathology
specimens. In addition, we provide a report on the package of methods that we have created for the extraction of the
glomerulus in the cortex.
This paper proposes a digital image analysis method to support quantitative pathology by automatically segmenting the hepatocyte structure and quantifying its morphological features. To structurally analyze histopathological hepatic images, we isolate the trabeculae by extracting the sinusoids, fat droplets, and stromata. We then measure the morphological features of the extracted trabeculae, divide the image into cords, and calculate the feature values of the local cords. We propose a method of calculating the nuclear–cytoplasmic ratio, nuclear density, and number of layers using the local cords. Furthermore, we evaluate the effectiveness of the proposed method using surgical specimens. The proposed method was found to be an effective method for the quantification of the Edmondson grade.
The steatosis in liver pathological tissue images is a promising indicator of nonalcoholic fatty liver disease (NAFLD) and the possible risk of hepatocellular carcinoma (HCC). The resulting values are also important for ensuring the automatic and accurate classification of HCC images, because the existence of many fat droplets is likely to create errors in quantifying the morphological features used in the process. In this study we propose a method that can automatically detect, and exclude regions with many fat droplets by using the feature values of colors, shapes and the arrangement of cell nuclei. We implement the method and confirm that it can accurately detect fat droplets and quantify the fat droplet ratio of actual images. This investigation also clarifies the effective characteristics that contribute to accurate detection.
The analysis of hepatic tissue structure is required for quantitative assessment of liver histology. Especially, a cord-like
structure of liver cells, called trabecura, has important information in the diagnosis of hepatocellular carcinoma (HCC).
However, the extraction of trabeculae is thought to be difficult because liver cells take on various colors and appearances depending on tissue conditions. In this paper, we propose an approach to extract trabeculae from images of hematoxyline and eosin stained liver tissue slide by extracting the rest of trabeculae: sinusoids and stromal area. The sinusoids are simply extracted based on the color information, where the image is corrected by an orientation selective filtering before segmentaion. The stromal area mainly consists of fiber, and often includes lymphocytes densely. Therefore, in the proposed method, fiber region and lymphocytes are extracted separately, then, stromal region is determined based on the extracted results. The determination of stroma is performed based on superpixels, to obtain precise boundaries. Once the regions of sinusoids and stroma are obtained, trabeculae can be segmented as the remaining region. The proposed method was applied to 10 test images of normal and HCC liver tissues, and the results were evaluated based on the manual segmentation. As a result, we confirmed that both sensitivity and specificity of the extraction of trabeculae reach around 90%.
Recent advances in information technology have improved pathological virtual-slide technology and diagnostic support system studies of pathological images. Diagnostic support systems utilize quantitative indices determined by image processing. In previous studies on diagnostic support systems, carcinomatous areas of breast or lung have been
recognized by the feature quantities of nuclear sizes, complexities, and internuclear distances based on graph theory,
among other features. Improving recognition accuracy is important for the addition of new feature quantities. We
focused on hepatocellular carcinoma (HCC) and investigated new feature quantities of histological images of HCC. One of the most important histological features of HCC is the trabecular pattern. For diagnosing cancer, it is important to recognize the tumor cell trabeculae. We propose a new algorithm for calculating the number of cell layers in histological images of HCC in tissue sections stained by hematoxylin and eosin. For the calculation, we used a Delaunay diagram that was based on the median points of nuclei, deleted the sinusoid and fat droplet regions from the Delaunay diagram, and counted the Delaunay lines while applying a thinning algorithm. Moreover, we experimented with the calculation of the number of cell layers with our method for different histological grades of HCC. The number of cell layers discriminated tumor differentiations and Edmondson grades; therefore, our algorithm may serve as an index of HCC for diagnostic support systems.