Segmentation of Cerebral Vessels and Aneurysms from MR Angiography Data
IPMI '97 Proceedings of the 15th International Conference on Information Processing in Medical Imaging
Computer-Aided Diagnosis of Intracranial Aneurysms in MRA Images with Case-Based Reasoning
IEICE - Transactions on Information and Systems
CAD on Brain, Fundus, and Breast Images
Medical Imaging and Informatics
Computer-aided diagnosis system for retinal diseases in medical imaging
WSEAS TRANSACTIONS on SYSTEMS
Computer-aided diagnosis: The emerging of three CAD systems induced by Japanese health care needs
Computer Methods and Programs in Biomedicine
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The purpose of our study is to develop an algorithm that would enable the automated detection of lacunar infarct on T1-and T2-weighted magnetic resonance (MR) images. Automated identification of the lacunar infarct regions is not only useful in assisting radiologists to detect lacunar infarcts as a computer-aided detection (CAD) system but is also beneficial in preventing the occurrence of cerebral apoplexy in high-risk patients. The lacunar infarct regions are classified into the following two types for detection: “isolated lacunar infarct regions” and “lacunar infarct regions adjacent to hyperintensive structures.” The detection of isolated lacunar infarct regions was based on the multiple-phase binarization (MPB) method. Moreover, to detect lacunar infarct regions adjacent to hyperintensive structures, we used a morphological opening processing and a subtraction technique between images produced using two types of circular structuring elements. Thereafter, candidate regions were selected based on three features — area, circularity, and gravity center. Two methods were applied to the detected candidates for eliminating false positives (FPs). The first method involved eliminating FPs that occurred along the periphery of the brain using the region-growing technique. The second method, the multi-circular regions difference method (MCRDM), was based on the comparison between the mean pixel values in a series of double circles on a T1-weighted image. A training dataset comprising 20 lacunar infarct cases was used to adjust the parameters. In addition, 673 MR images from 80 cases were used for testing the performance of our method; the sensitivity and specificity were 90.1% and 30.0% with 1.7 FPs per image, respectively. The results indicated that our CAD system for the automatic detection of lacunar infarct on MR images was effective.