Computed Automatic 3D Segmentation Methods in Computed Tomography Laser Mammography

Jalalian, Afsaneh; Ramli, Abdul-Rahman; Karasfi, Babak; Bahri, Nandini; Mahmud, Rozi; Suppiah, Subapriya; Mashohor, Syamsiah

doi:10.15224/978-1-63248-064-4-110

Cited by 1 publication

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“…Segmentation is the first stage of the proposed CAD system in CTLM images which has been evaluated in our previous works [19,20]. The results illustrate that 3D FCM surpasses K-mean clustering and colour quantization technique by providing maximum values of 98.49 and 99.24% and minimum values of 90.87 and 95.14% for Jaccard and Dice indexes.…”

Section: Resultsmentioning

confidence: 95%

“…The 2D segmentation techniques are not capable of displaying different forms of angiogenesis in CTLM images; therefore, the 3D automatic segmentation techniques are of great interest in the segmentation of CTLM images. The three automatic segmentation techniques include Fuzzy C-means clustering (FCM), K-mean clustering and colour quantization method [18] and have been implemented in our previous study [19,20] to extract volume of interest from CTLM images. In order to accept the segmentation results in clinical practice, the quantitative evaluation is crucially important.…”

Section: D Segmentation Of Volume Of Interests From Ctlm Backgroundmentioning

confidence: 99%

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Computer-Assisted Diagnosis System for Breast Cancer in Computed Tomography Laser Mammography (CTLM)

et al. 2017

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Computed tomography laser mammography (Eid et al. Egyp J Radiol Nucl Med, 37(1): p. 633-643, 1) is a non-invasive imaging modality for breast cancer diagnosis, which is time-consuming and challenging for the radiologist to interpret the images. Some issues have increased the missed diagnosis of radiologists in visual manner assessment in CTLM images, such as technical reasons which are related to imaging quality and human error due to the structural complexity in appearance. The purpose of this study is to develop a computer-aided diagnosis framework to enhance the performance of radiologist in the interpretation of CTLM images. The proposed CAD system contains three main stages including segmentation of volume of interest (VOI), feature extraction and classification. A 3D Fuzzy segmentation technique has been implemented to extract the VOI. The shape and texture of angiogenesis in CTLM images are significant characteristics to differentiate malignancy or benign lesions. The 3D compactness features and 3D Grey Level Co-occurrence matrix (GLCM) have been extracted from VOIs. Multilayer perceptron neural network (MLPNN) pattern recognition has developed for classification of the normal and abnormal lesion in CTLM images. The performance of the proposed CAD system has been measured with different metrics including accuracy, sensitivity, and specificity and area under receiver operative characteristics (AROC), which are 95.2, 92.4, 98.1, and 0.98%, respectively.

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Section: Resultsmentioning

confidence: 95%

Section: D Segmentation Of Volume Of Interests From Ctlm Backgroundmentioning

confidence: 99%

Computer-Assisted Diagnosis System for Breast Cancer in Computed Tomography Laser Mammography (CTLM)

et al. 2017

View full text Add to dashboard Cite

show abstract

Computed Automatic 3D Segmentation Methods in Computed Tomography Laser Mammography

Cited by 1 publication

References 0 publications

Computer-Assisted Diagnosis System for Breast Cancer in Computed Tomography Laser Mammography (CTLM)

Computer-Assisted Diagnosis System for Breast Cancer in Computed Tomography Laser Mammography (CTLM)

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