Shinya Maeda

Automatic detection of ground glass opacity from the thoracic MDCT images by using density features

Automatic detection of abnormal shadow area on a multi detector CT image is important task under developing a computer aided diagnosis system. Ground glass opacity is one of the important features in lung cancer diagnosis of computer aided diagnosis. It may be seen as diffuse or more often as patchy in distribution taking sometimes a geographic or mosaic distribution. A large number of diseases can be associated with GGO on CT image. We propose a technique for automatic detection of ground glass opacity from the segmented lung regions by computer based on a set of the thoracic CT images. In this paper, we segment the lung region for extraction of the region of interest employing binarization and labeling process from the inputted each slices images. The region having the largest area is regarded as the tentative lung regions. Furthermore, the ground glass opacity is classified by correlation distribution on the successive slice from the extracted lung region with respect to the thoracic CT images. Experiment is performed employing 32 thoracic CT image sets and 71.7% of recognition rates were achieved. Obtained results are shown along with a discussion.

Detection of Lung Nodule on Temporal Subtraction Images Based on Artificial Neural Network

The temporal subtraction technique as one of computer aided diagnosis has been introduced in medical fields to enhance the interval changes such as formation of new lesions and changes in existing abnormalities on deference image. With the temporal subtraction technique radiologists can easily detect lung nodules on visual screening. Until now, two-dimensional temporal subtraction imaging technique has been introduced for the clinical test. We have developed new temporal subtraction method to remove the subtraction artifacts which is caused by mis-registration on temporal subtraction images of lungs on MDCT images. In this paper, we propose a new computer aided diagnosis scheme for automatic enhancing the lung nodules from the temporal subtraction of thoracic MDCT images. At first, the candidates regions included nodules are detected by the multiple threshold technique in terms of the pixel value on the temporal subtraction images. Then, a rule-base method and artificial neural networks is utilized to remove the false positives of nodule candidates which is obtained temporal subtraction images. We have applied our detection of lung nodules to 30 thoracic MDCT image sets including lung nodules. With the detection method, satisfactory experimental results are obtained. Some experimental results are shown with discussion.

Automatic Segmentation of Liver Region Employing Rib Cage and Its 3-D Displaying

In recent years, computer aided diagnosis system is developed by using medical image processing techniques. Especially, computed tomography images have been used for diagnosis of liver disease or volume measurement for liver surgery. In CAD system, segmentation of a region of interests is one of the important techniques as pre-processing for extraction of abnormal area, in the abdominal CT image field, there are some regions that having ambiguous boundary such as liver, spleen, and gallbladder. It is difficult to segment such regions including some other soft tissue because one organ is very similar to the other, if a part of liver region is close to the tissue around rib bone, the boundary of region of interest area is very ambiguous. In this paper, we propose a technique for automatic segmentation of the liver region employing the rib cage area and active contour model on the abdominal CT images. By employing the rib cage information instead of ambiguous boundary, we can reduce the segmentation errors. Furthermore, we develop a system for 3D displaying of the segmented region. Experiment is performed employing 4 abdominal MDCT image sets and the obtained results are shown with some discussion

Automatic detection of GGO regions on CT images in LIDC dataset based on statistical features

Detection of pulmonary nodules with ground glass opacity (GGO) is a difficult task in radiology. Follow up is often required in medical fields. But diagnosis based on CT images are dependent on ability and experience of radiologists. In addition to that, enormous number of images increase their burden. So, to improve the detection accuracy and to reduce the burden of doctors, a CAD (Computer Aided Diagnosis) system is expected. So, in this paper, we propose an automatic algorithm for GGO detection on CT images. At first, vessel areas are removed from original CT images by using 3D Line Filter and then candidate regions are detected by threshold processing. After that, we calculate statistical features of segmented candidate regions and use artificial neural network (ANN) to distinguish final candidate regions. We applied the proposed method to 31 CT image sets in the Lung Image Database Consortium (LIDC) which is supplied by National Center Institute (NCI). In this paper, we show the experimental results and give discussions.

Nonrigid image registration method for thoracic CT images using vessel structure information

Temporal Subtraction Technique is one of effective tools for diagnosis of lung cancer from thoracic CT images. By comparing two images of the same subject but shot at different time, the detection of temporal changing becomes facilitated. To acquire a more accurate subtraction image, the registration of these two images is critical. However it is not easy as the influence of the slight structures such as lung blood vessel. In this paper, a novel nonrigid image registration method based on vessel structure information is proposed. The similarity of the vessels structure is defined by means of the likelihood function of vessels structure and their direction. We combine this similarity of vessels with the intensity information of images. And the metrics are used as similarity measure in registration procedure. The proposed method has been applied to thoracic MDCT images, and the improvement of registration accuracy was investigated. The efficiency of our proposed method was indicated.

Detection of Blood Vessels on the Abdominal CT Images Employing Temporal Subtraction Method

In recent years, various imaging techniques have been introduced into medical fields for extraction of abdominal area such as magnetic resonance imaging, high resolution helical computed tomography scanners with multi-detector rows, and positron emission tomography etc. Especially, HRCT is one of the useful diagnosis systems because it provides high resolution images to physician. In the field of abdominal CT images, it is very important to understand blood vessel structures of a patient before operation. Furthermore, in the medical imaging field, segmentation is one of the most important problems. Many related segmentation techniques have been developed for automatic extraction of regions of interest. On the other hand, temporal subtraction method attempt to remove normal structures in the CT images, so that abnormalities can be observed more clearly to the medical doctor, it is mainly applied to chest radiography image. With the subtraction technique, image warping for accurately deforming the previous image to match the current image is very important. If the warping is incorrect, normal structures will produce artifacts in the subtraction image, and the image quality can be degraded. In this paper, we apply temporal subtraction method to abdominal CT images in order to detect the blood vessel region for analyzing the internal organ. In this method, the temporal subtraction method is applied between successive phases. The proposed technique is applied to four abdominal CT images and satisfactory results are achieved