Cardiac scintigraphic images segmentation techniques

Fadhel, Yosra Ben; Ktata, S.; Kraiem, Tarek

doi:10.1109/atsip.2016.7523107

Cited by 7 publications

(3 citation statements)

References 12 publications

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“…The traditional segmentation task in DM can be divided into four main classes: (i) threshold-based segmentation; (ii) region-based segmentation; (iii) pixel-based segmentation; and (iv) model-based segmentation [3,72]. Otherwise, US image segmentation includes several techniques: threshold-based, region-based, edge-based, water-based, active contour and neural network learning-based techniques [89,90].…”

Section: Image Segmentationmentioning

confidence: 99%

Deep-Learning-Based Computer-Aided Systems for Breast Cancer Imaging: A Critical Review

2020

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This paper provides a critical review of the literature on deep learning applications in breast tumor diagnosis using ultrasound and mammography images. It also summarizes recent advances in computer-aided diagnosis/detection (CAD) systems, which make use of new deep learning methods to automatically recognize breast images and improve the accuracy of diagnoses made by radiologists. This review is based upon published literature in the past decade (January 2010–January 2020), where we obtained around 250 research articles, and after an eligibility process, 59 articles were presented in more detail. The main findings in the classification process revealed that new DL-CAD methods are useful and effective screening tools for breast cancer, thus reducing the need for manual feature extraction. The breast tumor research community can utilize this survey as a basis for their current and future studies.

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Section: Image Segmentationmentioning

confidence: 99%

Deep-Learning-Based Computer-Aided Systems for Breast Cancer Imaging: A Critical Review

2020

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“…This has led to difficulty in the task of segmenting tumor from the processed images. The task of image segmentation involves separating background and objects that are present in the images, and to split image I into several subsets named regions Ri [24], [25] thresholding, and region split and merge. All these steps would eventually produce segmented image into regions based on pixel similarity [29].…”

Section: Bus Image Segmentation Techniquesmentioning

confidence: 99%

A Survey of Ultrasonography Breast Cancer Image Segmentation Techniques

Saeed

2020

ACAD J NAWROZ UNIV

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The most common cause of death among women globally is breast cancer. One of the key strategies to reduce mortality associated with breast cancer is to develop effective early detection techniques. The segmentation of breast ultrasound (BUS) image in Computer-Aided Diagnosis (CAD) systems is critical and challenging. Image segmentation aims to represent the image in a simplified and more meaningful way while retaining crucial features for easier analysis. However, in the field of image processing, image segmentation is a tough task particularly in ultrasound (US) images due to challenges associated with their nature. This paper presents a survey on several techniques of ultrasonography images segmentation including threshold based, region based, watershed, active contour and learning based techniques, their merits, and demerits. This can provide significant insights for CAD developers or researchers to advance this field.

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“…In [19], the author applied different segmentation techniques in the aim of reorganization of the left ventricle in a VEF image. He compared two segmentation approaches: region-based segmentation and edge-based segmentation.…”

Section: Related Work 21 General Segmentation and Face Segmentationmentioning

confidence: 99%