Histopathological Image Analysis Using Image Processing Techniques: An Overview

Belsare, Amoli D.; Mushrif, Milind M.

doi:10.5121/sipij.2012.3403

Cited by 68 publications

(64 citation statements)

References 33 publications

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“…Figure 14) and in medical sciences, automated classification of histology imagery is used to find malignant tumors in an early stage (e.g. Belsare and Mushrif , 2012). (Petropoulos et al, 2012) Compared to existing coastal image analysis techniques the proposed algorithm uses an abundance of pixel features and relations between neighboring superpixels to distinguish one class of pixels from another.…”

Section: Discussionmentioning

confidence: 99%

An automated method for semantic classification of regions in coastal images

Hoonhout

Radermacher

Baart

et al. 2015

Coastal Engineering

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Large, long-term coastal imagery datasets are nowadays a low-cost source of information for various coastal research disciplines. However, the applicability of many existing algorithms for coastal image analysis is limited for these large datasets due to a lack of automation and robustness. Therefore manual quality control and site-and time-dependent calibration is often required. In this paper we present a fully automated algorithm that classifies each pixel in an image given a pre-defined set of classes. The necessary robustness is obtained by distinguishing one class of pixels from another based on more than a thousand pixel features and relations between neighboring pixels rather than a handful of color intensities.Using a manually annotated dataset of 192 coastal images, a Structured Support Vector Machine (SSVM) is trained and tested to distinguish between the classes water, sand, vegetation, sky and object. The resulting model correctly classifies 93.0% of all pixels in a previously unseen image. Two case studies are used to show how the algorithm extracts beach widths and water lines from a coastal camera station.Both the annotated dataset and the software developed to perform the model training and prediction are provided as free and open-source data sources.

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Section: Discussionmentioning

confidence: 99%

An automated method for semantic classification of regions in coastal images

Hoonhout

Radermacher

Baart

et al. 2015

Coastal Engineering

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“…Based on the processing primitives, we can categorize previous gland segmentation approaches into two types [9]: pixelbased method [1]- [3] and object-based method [5], [10], [11]. For the pixel-based method, the algorithm tries to assign each Fig.…”

Section: Related Workmentioning

confidence: 99%

“…The new pairwise potential is defined as (8) where is a function over . It is formally defined as (9) where and are all constants. From the above definition of , it can be seen that when approaches 0, approaches , and when approaches approaches…”

Section: Pairwise Potentialmentioning

confidence: 99%

A Novel Polar Space Random Field Model for the Detection of Glandular Structures

Qiu

Shu

et al. 2014

IEEE Trans. Med. Imaging

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Abstract-In this paper, we propose a novel method to detect glandular structures in microscopic images of human tissue. We first convert the image from Cartesian space to polar space and then introduce a novel random field model to locate the possible boundary of a gland. Next, we develop a visual feature-based support vector regressor to verify if the detected contour corresponds to a true gland. And finally, we combine the outputs of the random field and the regressor to form the GlandVision algorithm for the detection of glandular structures. Our approach can not only detect the existence of the gland, but also can accurately locate it with pixel accuracy. In the experiments, we treat the task of detecting glandular structures as object (gland) detection and segmentation problems respectively. The results indicate that our new technique outperforms state-of-the-art computer vision algorithms in respective fields.

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“…The examination of the tissue structures has been shown in the citation by A. D. Belsare et al, [3], and the cell distribution in a tissue, irregularities of the shapes of cells to determine the level of malignancy and benign in a histopathology image slide is a very time consuming process that is prone to intra as well as inter observation variations. To overcome this kind of a difficulty a CAD image analysis is often required for quantitative diagnosis of tissues and cells.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Slow scanner speed, poor image quality, low network bandwidth and high cost have meant that digital pathology systems had no cost or efficiency advantage over the current process. In M. Gurcan et al, [1], and citations of various other authors [2,3,4] has shown the improvement in image analysis over the past decades due to the use of digitized image analysis and the crucial role of computer-aided diagnosis (CAD). Recent innovations in digital pathology technology have radically altered this picture by transcending these earlier limitations, thus opening new horizons in anatomic pathology.…”

Section: Introductionmentioning

confidence: 99%