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Segmentation of pectoral muscle from digital mammograms with depth-first search algorithm towards breast density classification

Pawar Shivaji D., Sharma Kamal Kr., Sapate Suhas G., Yadav Geetanjali Y.

Biocybernetics and Biomedical Engineering

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2021

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Vol. 41, no. 3

1224--1241

EN

Digital mammography acts as a unique screening technology to protect the lives of females against breast cancer for the past few decades. Mammographic breast density is a well-known biomarker and plays a substantial role in breast cancer prediction and treatments. Breast density is calculated based on the opacity of fibro-glandular tissue reflected on digital mammograms concerning the whole area of the breast. The opacity of pectoral muscle and fibro-glandular tissue is similar to each other; hence, the small presence of the pectoral muscle in the breast area can hamper the accuracy of breast density classification. Successful removal of pectoral muscle is challenging due to changes in shape, size, and texture of pectoral muscle in every MLO and LMO views of mammogram. In this article, the depth-first search (DFS) algorithm is proposed to remove artifacts and pectoral muscle from digital mammograms. In the proposed algorithm, image enhancement is performed to improve the pixel quality of the input image. The whole breast as a single connected component is identified from the background region to remove the artifacts and tags. The depth-first search method with and without the heuristic approach is used to delineate the pectoral muscle, and then final suppression is performed on it. This algorithm is tested on 2675 images of the DDSM dataset, which is further divided into four density classes as per BIRADs classification. Segmentation results are calculated individually on each BIRADs density class of the DDSM dataset. Results are validated subjectively by the expert’s Radiologist’s ground truth with segmentation accuracy and objectively by the Jaccard coefficient and a dice similarity coefficient. This algorithm is found robust on each density class and provides overall segmentation accuracy of 86.18%, a mean value of Jaccard index, and a Dice similarity coefficient of 0.9315 and 0.9548, respectively. The experimental results show that the proposed algorithms applied for pectoral muscle removal follow the ground truth marked by an expert radiologist. The proposed algorithm can be part of the pre-processing unit of breast density measurement and breast cancer detection system used during clinical practice.

2

Efficient Tree Coding Algorithms

Wang X., Tian J.

Przegląd Elektrotechniczny

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2012

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R. 88, nr 7b

350--352

EN

This paper studies the algorithms for coding and decoding second Neville’s codes of a labeled tree. The algorithms for coding and decoding second Neville’s codes of a labeled tree in the literatures require O(n log n) time usually. As stated in [1][2], no linear time algorithms for the second Neville’s codes. In this paper we consider the second Neville’s code problem in a different angle and a more direct manner. We start from a naïve algorithm, then improved it gradually and finally we obtain a very practical linear time algorithm. The techniques we used in this paper are interesting themselves.

PL

W artykule rozważano problem kodu Neville drugiego rzędu stosowanego do etykietowania elementów struktury typu drzewo.

3

Numbering action vertices in workflow graphs

Mann Z. Á.

International Journal of Applied Mathematics and Computer Science

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2010

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Vol. 20, no 3

591-600

EN

Workflow graphs, consisting of actions, events, and logical switches, are used to model business processes. In order to easily identify the actions within a workflow graph, it is useful to number them in such a way that the numbering reflects the structure of the workflow. However, available tools offer only rudimental numbering schemes. In the paper, a set of natural requirements is defined that a logical numbering should fulfill. It is investigated under what conditions there is an appropriate numbering at all, when it is uniquely defined by the set of requirements, and when it can be computed efficiently. It is shown that for an important special class of workflow graphs, namely, structured workflow graphs, the answer to all these questions is affirmative. For general workflow graphs, a set of requirements is presented that can always be fulfilled, but the numbering is not necessarily unique. An algorithm based on a depth-first search can be used to compute an appropriate numbering efficiently.