An improved Chan-Vese model without reinitialization for medical image segmentation

Zhao, Ji; Shao, Fuqun; Yang, Xu; Zhang, Xuedong; Huang, Wenge

doi:10.1109/cisp.2010.5647991

Cited by 6 publications

(2 citation statements)

References 18 publications

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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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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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“…FCM is the most widely used image segmentation method. FCM can retain more image information and has robust fuzzy characteristics and the advantages of simple structure, easy implementation, and good segmentation effect [35]. Image segmentation by FCM can be judged by the fuzziness of pixel points to attribute, reduce human intervention, be suitable for segmentation of images with fuzziness and uncertainty, and improve the effectiveness of fuzzy clustering power [36].…”

Section: Introductionmentioning

confidence: 99%

Solar radio spectrogram segmentation algorithm based on improved fuzzy C-means clustering and adaptive cross filtering

Liu,

Shen,

Song

et al. 2024

Phys. Scr.

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Solar radio spectrograms contain essential information, such as the duration type; therefore, recognizing and detecting solar radio spectrograms are significant for the further study of solar radio. With the upgrading of solar radio observation, considering the equipment that has already generated amounts of data, researchers have begun to use machine learning methods to recognize and detect solar radio spectrograms to resolve the weaknesses of manual identification, such as time consumption. However, the spectrograms are characterized by noise or insignificant outburst features, which affect the recognition and detection of solar radio spectrograms. In contrast, extracting the burst region separately and the more distinctive spectrogram features will help identify and detect it. Therefore, to remove the burst domain of the radio spectrogram better, this paper combines the idea of image segmentation and proposes a solar radio spectrogram segmentation algorithm based on improved fuzzy C-means (FCM) clustering and adaptive cross filtering for the extraction of the burst domain of solar radio spectrograms. This algorithm has multiple processing steps. The first step is solar radio spectrogram segmentation with the improved FCM based on the kernel-induced distance by incorporating spatial constraints combined with random walk and adaptive affiliation linking (RWAKFCM_S). The second step is adaptive cross filtering, eliminating the noise clustered in bursts. The results show the following. (1) The RWAKFCM_S proposed in this paper has better anti-noise and segmentation performance than other methods in the synthetic, natural, and solar radio spectrogram segmentation experiments; it can also overcome the problems of noise sensitivity when segmenting spectrograms by traditional FCM. (2) The RWAKFCM_S can satisfy the high accuracy and rate of solar radio spectrogram segmentation demands. (3) The adaptive cross filtering proposed in this paper can eliminate noise clustered in the eruption domain. (4) The proposed method enables burst region extraction。

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