An algorithm for segmentation of lung ROI by mean-shift clustering combined with multi-scale Hessian matrix dot filtering

WEI Ying(魏颖)^1,2, LI Rui(李锐) ¹, YANG Jin-zhu(杨金柱) ² , ZHAO Da-zhe(赵大哲) ²

(1. School of Information Science and Engineering, Northeastern University, Shenyang 110004, China;
2. Key Laboratory of Medical Imaging Calculation of Ministry of Education
(Northeastern University), Shenyang 110004, China)

Abstract:A new algorithm for segmentation of suspected lung ROI (regions of interest) by mean-shift clustering and multi-scale Hessian matrix dot filtering was proposed. Original image was firstly filtered by multi-scale Hessian matrix dot filters, round suspected nodular lesions in the image were enhanced, and linear shape regions of the trachea and vascular were suppressed. Then, three types of information, such as, shape filtering value of Hessian matrix, gray value, and spatial location, were introduced to feature space. The kernel function of mean-shift clustering was divided into product form of three kinds of kernel functions corresponding to the three feature information. Finally, bandwidths were calculated adaptively to determine the bandwidth of each suspected area, and they were used in mean-shift clustering segmentation. Experimental results show that by the introduction of Hessian matrix of dot filtering information to mean-shift clustering, nodular regions can be segmented from blood vessels, trachea, or cross regions connected to the nodule, non-nodular areas can be removed from ROIs properly, and ground glass object (GGO) nodular areas can also be segmented. For the experimental data set of 127 different forms of nodules, the average accuracy of the proposed algorithm is more than 90%

Key words:Hessian matrix; multi-scale dot filtering; mean-shift clustering; segmentation of suspected areas; lung computer-aided detection/diagnosis