3D reconstruction for volume of interest in computed tomography laser mammography images

Abstract

Computer assisted diagnosis systems (CADs) is now commonly used as a second opinion to help radiologists in image interpretation by emphasizing on the suspicious areas. Segmentation of region of interests in 2-dimensional (2D) or volume of interests in 3-dimensional (3D) images is a critical step in CAD systems. 3D image segmentation using 2D slices has been a keen of interest for research purpose. In this paper we propose to reconstruct a 3D form of volume of interests (VOIs) from a series of 2D images in computed tomography laser mammography (CTLM). In this paper, a 3D Fuzzy C-Means clustering have been implemented to reconstruct VOIs for breast cancer detection in CTLM images.To assess the accuracy of the extracted VOIs against ground truth, percentage error factor is used and produced error value of 10.72% in our dataset of 62 CTLM breast images collected among Malaysian participants.

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

“…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.…”

“…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.…”