Distributed PACS using distributed file system with hierarchical meta data servers.

In this research, we propose a new distributed PACS (Picture Archiving and Communication Systems) which is available to integrate several PACSs that exist in each medical institution. The conventional PACS controls DICOM file into one data-base. On the other hand, in the proposed system, DICOM file is separated into meta data and image data and those are stored individually. Using this mechanism, since file is not always accessed the entire data, some operations such as finding files, changing titles, and so on can be performed in high-speed. At the same time, as distributed file system is utilized, accessing image files can also achieve high-speed access and high fault tolerant. The introduced system has a more significant point. That is the simplicity to integrate several PACSs. In the proposed system, only the meta data servers are integrated and integrated system can be constructed. This system also has the scalability of file access with along to the number of file numbers and file sizes. On the other hand, because meta-data server is integrated, the meta data server is the weakness of this system. To solve this defect, hieratical meta data servers are introduced. Because of this mechanism, not only fault--tolerant ability is increased but scalability of file access is also increased. To discuss the proposed system, the prototype system using Gfarm was implemented. For evaluating the implemented system, file search operating time of Gfarm and NFS were compared.

Measurements of right / left hemisphere activation using functional near-infrared spectroscopy during incongruent Stroop test.

The human brain automatically processes information at multiple sites when recognizing various types of information at the same time, such as color, shape, etc. Cognitive conflict may occur when conflicting information is recognized at the same time. Functional near-infrared spectroscopy (fNIRS) is often used to examine the brain activity associated with this phenomenon. To examine activation of the visual system, we measured brain activity in the right / left hemispheres during cognitive conflict in the Stroop test. Consistent with the results reported previously by Ehlis, the brain activity in the near inferior-frontal gyrus of the left hemisphere was increased during the incongruent task. The brain activity also increased in the near inferior-frontal gyrus of the right hemisphere during the incongruent task. These results indicated that fNIRS can be used to detect brain activity in the inferior-frontal gyrus of the right / left hemispheres during the Stroop test.

An algorithm for cancer nest feature extraction from pathological images.

Here, we propose an algorithm to automatically obtain extraction filters for the affected regions from cancer images. The proposed algorithm consists of two steps: extraction of affected region candidates and elimination of false positives. Useful features of cancer images, such as the area and degree of circularity of cancer nests, etc., are extracted using the derived filters. These features are useful for supporting pathological diagnosis. Automatic Construction of Tree-structural Image Transformation (ACTIT) was used to construct these filters to extract the affected regions from the image. The proposed algorithm was applied to a mouth cancer pathological image. The results confirmed that the proposed algorithm can obtain good filters capable of extracting cancer nests. The derived filters were also applied to other images from the same specimen. The results also indicated that the generated filters show general versatility in extracting cancer nest candidates. The area and degree of circularity of the cancer nets were also derived automatically.