Breast MRI Tumour Segmentation Using Modified Automatic Seeded Region Growing Based on Particle Swarm Optimization Image Clustering

Al-Faris, Ali Qusay; Ngah, Umi Kalthum; Isa, Nor Ashidi Mat; Shuaib, Ibrahim Lutfi

doi:10.1007/978-3-319-00930-8_5

Cited by 39 publications

(18 citation statements)

References 17 publications

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“…Tumor size estimation is liable to potential blunders, and both tumor attributes and imaging constraints may differentially influence the estimation exactness of tests utilized for this reason. Underestimation of tumor size may in this manner lead to included surgical margins; overestimation may prompt excessively radical medical procedure (counting mastectomy when BCS may have been conceivable), and poorer psychosocial/ cosmetic results [2]. The modality which is frequently used for the breast radiologist"s daily clinical practice is breast MRI.…”

Section: Inspiring Factorsmentioning

confidence: 99%

ROI Selection Criteria for Finding the Abnormal Tissues from Breast Magnetic Resonance Imaging

Jaglan¹,

Dass²,

Duhan³

2019

IJEAT

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The imaging methods in breast diagnostics play a preeminent role in the early detection and finding out the exact location & area of the suspicious breast tissues for malignancy. The further treatment significantly depends on the tumour-tobreast size relationship. The tumor size considered as the most influential factors for pathological/clinical assessment of breast cancer. In general, localization of the tumor's location and also the selection of a region of interest (ROI) were performed manually by an experienced radiologist. The objective of this paper is to propose an effective criterion for selection of ROI for abnormal tissues detection from breast MRI. This paper implements an efficacious ROI selection criterion for finding the exact location & area of the breast abnormal tissues from magnetic resonance imaging automatically. The proposed algorithm integrates the simple techniques like filtering, edge detection and morphological operations for inner segmentation. Outer breast region segmentation is performed by selecting the peak and valley points and then connects the selected points by applying fit to circle function which makes the MR image rotation invariant. The method is implemented on the 80 images contained in S1 dataset i.e. multi-parametric breast MRI dataset and the evaluation is done through comparative analysis of predicted image with manually segmented images. The experimental results in terms of evaluation matrices i.e. Precision, Recall and Score depict the efficacy of the proposed work.

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Section: Inspiring Factorsmentioning

confidence: 99%

ROI Selection Criteria for Finding the Abnormal Tissues from Breast Magnetic Resonance Imaging

Jaglan¹,

Dass²,

Duhan³

2019

IJEAT

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“…In Table 2(Tab. 2) (References in Table 2: Al-Faris et al, 2014[6]; Kim et al, 2014[75]; Dheeba et al, 2014[34]; Al-Faris et al, 2014[7]; Hassanien et al, 2014[57]; Kannan et al, 2011[73]) a few examples of segmentation methods in breast CAD systems collected are shown. …”

Section: Cornerstones Of a Cad Systemmentioning

confidence: 99%

Foundation and methodologies in computer-aided diagnosis systems for breast cancer detection

Jalalian

Mashohor

Mahmud

et al. 2017

EXCLI Journal; 16:Doc113; ISSN 1611-2156

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“…A number of segmentation methods have been developed to segment out suspicious masses in mammographic images, region growing (Al-Faris et al,2014), normalize cut (Don et al,2011), level sets (Yuan et al,2007), active contours (Wirth and Stapinski 2003) and dynamic programming (Timp and Karssemeijer 2004). Among of them, active contour algorithm is a very popular method.…”

Section: Related Workmentioning

confidence: 99%

Breast Mass Segmentation in Mammographic Images Based on Gmac Model using Variational Level Set Method

2016

Rev. Téc. Ing. Univ. Zulia.

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Breast cancer is the most common form of cancer in the female population. Mammography is recognized as the most effective method for its diagnosis. However, it is a very difficult, time-consuming to interpret mammographic images. Computer-aided detecting and diagnosis (CADx) systems have the potential to assist radiologists in the complex task of discriminating malignant and benign types of breast lesions and reducing the biopsy rate without increasing false negatives. In most types of CADx systems, a step is computing mammographic images features which automatically extracted from regions of interest (ROIs) depicting mammographic masses. Therefore, mass segmentation is a very important step for classification of suspicious regions as normal, benign or malignant, which influences the following classification and detection directly. Although so many mass segmentation algorithms have been proposed for mass segmentation in mammography, it is still a difficult and challenge problem to achieve accurate segmentation results. The purpose of this study was to develop an automated method for mammographic mass segmentation. The approach is based on Global minimum active contour (Gmac) model, and using variational level set method to solve the curve evolution problem derived by minimizing the curve energy. At last, a lot of experiments were carried out. Those segmentation results demonstrated the effectiveness of the proposed method.

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Breast MRI Tumour Segmentation Using Modified Automatic Seeded Region Growing Based on Particle Swarm Optimization Image Clustering

Cited by 39 publications

References 17 publications

ROI Selection Criteria for Finding the Abnormal Tissues from Breast Magnetic Resonance Imaging

ROI Selection Criteria for Finding the Abnormal Tissues from Breast Magnetic Resonance Imaging

Foundation and methodologies in computer-aided diagnosis systems for breast cancer detection

Breast Mass Segmentation in Mammographic Images Based on Gmac Model using Variational Level Set Method

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