Ploidy analysis on digital slides

Jónás, Viktor; Kozlovszky, Miklós; Molnár, Béla

doi:10.1109/cinti.2013.6705207

Cited by 9 publications

(3 citation statements)

References 5 publications

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“…Same in the proposed method and the other half of the "Split" class 1 False negative: to a reference there is no measurement object. Same in the proposed method Other: all other cases, usually where one measurement corresponds to more reference objects.…”

Section: Definition Of the Evaluation Classesmentioning

confidence: 91%

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Semi-Automated Quantitative Validation Tool for Medical Image Processing Algorithm Development

Jónás

Kozlovszky

Molnár

2015

IFIP Advances in Information and Communication Technology

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Abstract. Cancer research and diagnostics is an important frontier to apply the power of computers. Researchers use image processing techniques for a few years now, but diagnostics only start to explore its possibilities. Pathologists specialized in this area usually diagnose by visual inspection, typically through a microscope, or more recently on a computer screen. They examine at tissue specimen or a sample consisting of a population cells extracted from it. The latter area is the area of cytometry that researchers started to support by creating image processing algorithms. The validation of an image processing approach like that is an expensive task both financially and time-wise. This paper aims to show a semi-automatized method to simplify this task, by reducing the amount of human interaction necessary.

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Section: Definition Of the Evaluation Classesmentioning

confidence: 91%

“…The project [1] we are currently participating in aims to reproduce a medical research and diagnostic method called ploidy analysis (PA) through image processing means. PA is a method to measure DNA content in cell nuclei as a basic cancer marker, and is considered as a segment of pathology, more closely image cytometry (ICM).…”

Section: Introductionmentioning

confidence: 99%

Semi-Automated Quantitative Validation Tool for Medical Image Processing Algorithm Development

Jónás

Kozlovszky

Molnár

2015

IFIP Advances in Information and Communication Technology

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“…Our aim is to help make the evaluation faster thus making it poss ible for experts to help more patients. There are already some solutions to identifying cells and other objects in the samples [2] [14], but most of them use either colour or shape descriptors to deal with the problem. We propose a solution where the texture of given regions is analyzed for the evaluation.…”

Section: Introductionmentioning

confidence: 99%

Framework for Comparison and Evaluation of Image Segmentation Algorithms for Medical Imaging

Windisch

Kozlovszky

2015

IFMBE Proceedings

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Abstract-Image segmentation algorithms are present in many image processing algorithms. Different algorithms achieve their goal differently, the usual method is to split the image into homogeneous segments based on some feature and then merge the neighboring segments that belong together. The present paper describes a method of comparison of image segmentation algorithms that focuses on the contour adherence accuracy of the given algorithms. A framework that has been designed and implemented is also presented here. The framework can and has been used for medical images (HE stained tissue digitalized with a microscope). The framework is presented using S LIC as a sample algorithm.

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Improvement of texture based image segmentation algorithm for HE stained tissue samples

Windisch

Kozlovszky

2013

2013 IEEE 14th International Symposium on Computational Intelligence and Informatics (CINTI)

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Ploidy analysis on digital slides

Cited by 9 publications

References 5 publications

Semi-Automated Quantitative Validation Tool for Medical Image Processing Algorithm Development

Semi-Automated Quantitative Validation Tool for Medical Image Processing Algorithm Development

Framework for Comparison and Evaluation of Image Segmentation Algorithms for Medical Imaging

Improvement of texture based image segmentation algorithm for HE stained tissue samples

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