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The computer-aided diagnostic (CAD) system can greatly influence the early detection of lung cancer in radiographs and computed tomography (CT) images. And the automatic nodule detection plays an important role in the CAD systems. This paper proposed a new enhancement filter (3D multi-scale Block LBP Filter) to detect the nodules in the lung regions. By taking advantages of the nodule regions' pixel...
Automatic pulmonary nodule detection in Computer Tomographic (CT) images is a challenge task for the Computer Aided Diagnosis (CAD) systems. This paper proposes a novel nodule enhancement filter (Homocentric Squares Filter) for automatic nodule detection based on CT characteristic and shape feature. First, the bright regions in the image are enhanced by calculating the CT value variation between an...
Pulmonary nodules are potential manifestation of lung cancer. Accurate segmentation of juxta-vascular nodules and ground glass opacity (GGO) nodules are an important and active area of research in medical image processing. At present, the classical segmentation algorithm of pulmonary nodules can not accurately obtain the boundary information of pulmonary nodules. In order to solve the problem, a new...
A computer-aided hepatocellular carcinoma system is introduced in this paper. Tumor size and tumor intensity changes are heavily referenced in clinical practice while diagnosing hepatocellular carcinoma. In the enhancement scanning, the tumor might have different intensity appearance in three consecutive phases: arterial phase, portal vein phase and delay phase. By comparing the intensity of tumor...
A novel algorithm is proposed in this paper in terms of the existing segmentation algorithm for pulmonary parenchyma of computed tomography (CT) image lacking automation. The images from blood vessels, bone, liver, heart and muscle composition on thoracic CT is characterized by great width and high gray-level, considering that, the proposed algorithm uses linear filtering to extract the outline of...
A semi-automatic method was developed for the segmentation of 3D gallbladders (GB) from CT images, in order to construct a patient-specific model for a surgical training system. First a support vector machine (SVM) classifier was trained to extract GB region from one single 2D slice in the intermediate part of a GB by voxel classification. Then the extracted GB contour, after some morphological operations,...
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