Automated segmentation of tissue images for computerized IHC analysis

This paper presents two automated methods for the segmentation ofimmunohistochemical tissue images that overcome the limitations of themanual approach aswell as of the existing computerized techniques. The first independent method, based on unsupervised color clustering, recognizes automatically the target cancerous areas in the specimen and disregards the stroma; the second method, based on colors separation and morphological processing, exploits automated segmentation of the nuclear membranes of the cancerous cells. Extensive experimental results on real tissue images demonstrate the accuracy of our techniques compared to manual segmentations; additional experiments show that our techniques are more effective in immunohistochemical images than popular approaches based on supervised learning or active contours. The proposed procedure can be exploited for any applications that require tissues and cells exploration and to perform reliable and standardized measures of the activity of specific proteins involved in multi-factorial genetic pathologie

Segmentation of Optic Disc in Fundus Images using Convolutional Neural Networks for Detection of Glaucoma

The condition of the vascular network of human eye is an important diagnostic factor in ophthalmology. Its segmentation in fundus imaging is a difficult task due to various anatomical structures like blood vessel, optic cup, optic disc, macula and fovea. Blood vessel segmentation can assist in the detection of pathological changes which are possible indicators for arteriosclerosis, retinopathy, microaneurysms and macular degeneration. The segmentation of optic disc and optic cup from retinal images is used to calculate an important indicator, cup-to disc ratio( CDR) accurately to help the professionals in the detection of Glaucoma in fundus images.In this proposed work, an automated segmentation of anatomical structures in fundus images such as blood vessel and optic disc is done using Convolutional Neural Networks (CNN) . A Convolutional Neural Network is a composite of multiple elementary processing units, each featuring several weighted inputs and one output, performing convolution of input signals with weights and transforming the outcome with some form of nonlinearity. The units are arranged in rectangular layers (grids), and their locations in a layer correspond to pixels in an input image. The spatial arrangement of units is the primary characteristics that makes CNNs suitable for processing visual information; the other features are local connectivity, parameter sharing and pooling of hidden units. The advantage of CNN is that it can be trained repeatedly so more features can be found. An average accuracy of 95.64% is determined in the classification of blood vessel or not. Optic cup is also segmented from the optic disc by Fuzzy C Means Clustering (FCM). This proposed algorithm is tested on a sample of hospital images and CDR value is determined. The obtained values of CDR is compared with the given values of the sample images and hence the performance of proposed system in which Convolutional Neural Networks for segmentation is employed, is excellent in automated detection of healthy and Glaucoma images

A color and shape based algorithm for segmentation of white blood cells in peripheral blood and bone marrow images

Cataloged from PDF version of article.Computer-based imaging systems are becoming important tools for quantitative assessment of peripheral blood and bone marrow samples to help experts diagnose blood disorders such as acute leukemia. These systems generally initiate a segmentation stage where white blood cells are separated from the background and other nonsalient objects. As the success of such imaging systems mainly depends on the accuracy of this stage, studies attach great importance for developing accurate segmentation algorithms. Although previous studies give promising results for segmentation of sparsely distributed normal white blood cells, only a few of them focus on segmenting touching and overlapping cell clusters, which is usually the case when leukemic cells are present. In this article, we present a new algorithm for segmentation of both normal and leukemic cells in peripheral blood and bone marrow images. In this algorithm, we propose to model color and shape characteristics of white blood cells by defining two transformations and introduce an efficient use of these transformations in a marker-controlled watershed algorithm. Particularly, these domain specific characteristics are used to identify markers and define the marking function of the watershed algorithm as well as to eliminate false white blood cells in a postprocessing step. Working on 650 white blood cells in peripheral blood and bone marrow images, our experiments reveal that the proposed algorithm improves the segmentation performance compared with its counterparts, leading to high accuracies for both sparsely distributed normal white blood cells and dense leukemic cell clusters. (C) 2014 International Society for Advancement of Cytometr

An Investigation of Image and Video Classification Algorithm for White Blood Cells Detection in Real Time View

Medical industry is struggling in finding the cure for many types of disease especially cancer. It is known that white blood cell is used to protect the body against bacteria and diseases. Nowadays, many ways in separating white blood cells in human body were introduced for example; centrifugation. In this report, the author is using a new approach in separating the white blood cells to help with the immune system of human body. The new approach used in separating it by using image classification algorithm to separate the white blood cells from the blood capillaries and it will be done live from a video. By separating the white blood cells, we can study the behaviour of the immune system since white blood cells is responsible for immune system in human body

Automated Detection of Acute Leukemia using K-mean Clustering Algorithm

Leukemia is a hematologic cancer which develops in blood tissue and triggers rapid production of immature and abnormal shaped white blood cells. Based on statistics it is found that the leukemia is one of the leading causes of death in men and women alike. Microscopic examination of blood sample or bone marrow smear is the most effective technique for diagnosis of leukemia. Pathologists analyze microscopic samples to make diagnostic assessments on the basis of characteristic cell features. Recently, computerized methods for cancer detection have been explored towards minimizing human intervention and providing accurate clinical information. This paper presents an algorithm for automated image based acute leukemia detection systems. The method implemented uses basic enhancement, morphology, filtering and segmenting technique to extract region of interest using k-means clustering algorithm. The proposed algorithm achieved an accuracy of 92.8% and is tested with Nearest Neighbor (KNN) and Naive Bayes Classifier on the data-set of 60 samples.Comment: Presented in ICCCCS 201

Recognizing white blood cells with local image descriptors

Automatic and reliable classification of images of white blood cells is desirable for inexpensive, quick and accurate health diagnosis worldwide. In contrast to previous approaches which tend to rely on image segmentation and a careful choice of ad hoc (geometric) features, we explore the possibilities of local image descriptors, since they are a simple approachthey require no explicit segmentation, and yet they have been shown to be quite robust against background distraction in a number of visual tasks. Despite its potential, this methodology remains unexplored for this problem. In this work, images are therefore characterized with the well-known visual bag-of-words approach. Three keypoint detectors and five regular sampling strategies are studied and compared. The results indicate that the approach is encouraging, and that both the sparse keypoint detectors and the dense regular sampling strategies can perform reasonably well (mean accuracies of about 80% are obtained), and are competitive to segmentation-based approaches. Two of the main findings are as follows. First, for sparse points, the detector which localizes keypoints on the cell contour (oFAST) performs somehow better than the other two (SIFT and CenSurE). Second, interestingly, and partly contrary to our expectations, the regular sampling strategies including hierarchical spatial information, multi-resolution encoding, or foveal-like sampling, clearly outperform the two simpler uniform-sampling strategies considered. From the broader perspective of expert and intelligent systems, the relevance of the proposed approach is that, since it is very general and problem-agnostic, it makes unnecesary human expertise to be elicited in the form of explicit visual cues; only the labels of the cell type are required from human domain experts

An Investigation of Image and Video Classification Algorithm for White Blood Cells Detection in Real Time View

Medical industry is struggling in finding the cure for many types of disease especially cancer. It is known that white blood cell is used to protect the body against bacteria and diseases. Nowadays, many ways in separating white blood cells in human body were introduced for example; centrifugation. In this report, the author is using a new approach in separating the white blood cells to help with the immune system of human body. The new approach used in separating it by using image classification algorithm to separate the white blood cells from the blood capillaries and it will be done live from a video. By separating the white blood cells, we can study the behaviour of the immune system since white blood cells is responsible for immune system in human body

Guided interactive image segmentation using machine learning and color based data set clustering

We present a novel approach that combines machine learning based interactive image segmentation using supervoxels with a clustering method for the automated identification of similarly colored images in large data sets which enables a guided reuse of classifiers. Our approach solves the problem of significant color variability prevalent and often unavoidable in biological and medical images which typically leads to deteriorated segmentation and quantification accuracy thereby greatly reducing the necessary training effort. This increase in efficiency facilitates the quantification of much larger numbers of images thereby enabling interactive image analysis for recent new technological advances in high-throughput imaging. The presented methods are applicable for almost any image type and represent a useful tool for image analysis tasks in general

Active mesh and neural network pipeline for cell aggregate segmentation

Segmenting cells within cellular aggregates in 3D is a growing challenge in cell biology due to improvements in capacity and accuracy of microscopy techniques. Here, we describe a pipeline to segment images of cell aggregates in 3D. The pipeline combines neural network segmentations with active meshes. We apply our segmentation method to cultured mouse mammary gland organoids imaged over 24 h with oblique plane microscopy, a high-throughput light-sheet fluorescence microscopy technique. We show that our method can also be applied to images of mouse embryonic stem cells imaged with a spinning disc microscope. We segment individual cells based on nuclei and cell membrane fluorescent markers, and track cells over time. We describe metrics to quantify the quality of the automated segmentation. Our segmentation pipeline involves a Fiji plugin that implements active mesh deformation and allows a user to create training data, automatically obtain segmentation meshes from original image data or neural network prediction, and manually curate segmentation data to identify and correct mistakes. Our active meshes-based approach facilitates segmentation postprocessing, correction, and integration with neural network prediction