In a previous study, we developed a hybrid tumor detection method that used both computed tomography (CT) and positron emission tomography (PET) images. However, similar to existing computer-aided detection (CAD) schemes, it was difficult to detect low-contrast lesions that touch to the normal organs such as the chest wall or blood vessels in the lung. In the current study, we proposed a novel lung tumor detection method that uses active contour filters to detect the nodules deemed "difficult" in previous CAD schemes. The proposed scheme detects lung tumors using both CT and PET images. As for the detection in CT images, the massive region was first enhanced using an active contour filter (ACF), which is a type of contrast enhancement filter that has a deformable kernel shape. The kernel shape involves closed curves that are connected by several nodes that move iteratively in order to enclose the massive region. The final output of ACF is the difference between the maximum pixel value on the deformable kernel, and pixel value on the center of the filter kernel. Subsequently, the PET images were binarized to detect the regions of increased uptake. The results were integrated, followed by the false positive reduction using 21 characteristic features and three support vector machines. In the experiment, we evaluated the proposed method using 100 PET/CT images. More than half of nodules missed using previous methods were accurately detected. The results indicate that our method may be useful for the detection of lung tumors using PET/CT images.
Breast cancer incidence tends to rise globally and the mortality rate for breast cancer is increasing in Japan. There are various screening modalities for breast cancer, and MRI examinations with high detection rate are used for high-risk groups, which are genetically prone to develop breast cancer. In the breast MRI examination, unenhanced T1 and T2 weighted images shows no significant difference in signal value between tumor and normal tissue. Therefore, tumors are identified with use of contrast enhanced kinetic curve obtained by dynamic scan using contrast agent. Some computer aided diagnosis methods using dynamic contrast enhanced MR images also have been proposed. However, contrast agent produces the allergic reaction in rare case; it should not be used for screening examinees. Here, MRI provides the anatomical and functional information by using various sequences without contrast agents. According to the reports, this information can discriminate between tumor and normal tissue. In this study, we analyzed unenhanced MR images by using plural sequences and developed an automated method for the detection of tumors. First, we extracted the breast region from the T1-weighted image semi-automatically. Next, using the threshold determined by considering the signal intensities of tumor and normal tissue, a thresholding method was applied for diffusion-weighted image to extract the first candidate regions. After labeling processing, the breast region removes outside candidates from Initial candidates. Then false positives are reduced by the rule-based classifier. Finally, we examined the remaining candidates as possible tumor regions. We applied the proposed method to 54 cases of MR images and evaluated its usefulness. As a result, the detection sensitivity was 71.9% and the abnormal regions were clearly detected. These results indicate that the proposed method may be useful for tumor detection in unenhanced breast MR images.
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