| Project/Area Number |
08672159
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
病態科学系歯学(含放射線系歯学)
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| Research Institution | Kyushu University |
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Principal Investigator |
MIWA Kunihiro Kyushu University Faculty of Dentistry Research asociate, 歯学部, 助手 (10136509)
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| Co-Investigator(Kenkyū-buntansha) |
SHIMIZU Mayumi Kyushu University Faculty of Dentistry Research asociate, 歯学部, 助手 (50253464)
TANAKA Takemasa Kyushu University Faculty of Dentistry Research asociate, 歯学部, 助手 (30163538)
TOKUMORI Kenji Kyushu University Faculty of Dentistry Research asociate, 歯学部, 助手 (40253463)
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| Project Period (FY) |
1996 – 1997
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| Project Status |
Completed (Fiscal Year 1997)
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| Budget Amount *help |
¥2,200,000 (Direct Cost: ¥2,200,000)
Fiscal Year 1997: ¥500,000 (Direct Cost: ¥500,000)
Fiscal Year 1996: ¥1,700,000 (Direct Cost: ¥1,700,000)
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| Keywords | pattern recoqnition / smoothing algorithms / texture analysis / カラードプラ像 / テクスチェア解析 / 定量的画像診断 / 均質度 / 粗造度 |
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| Research Abstract |
The new measure for texture analysis is based on the intuition that the important texture information for the human visual system is contained in the relative frequency of local extremas in intensity. The principal measurement in this process is the determination of the number of local gray level maximum and minimum along a one-dimensional scan direction. The gray level values are first sent through a smoothing process which eliminates reversals of small amplitude, thereby retaining only the principal extrema. The smoothing algorithm is the digital equivalent of the familiar analog mechanical process known as gear backlash and it was originally described as a preprocessing method for character recognition.
The ultrasonographic images of the tissue-mimicking phantom, normal parotid and submandibular glands, sialadenitis and salivary gland tumors were used. The images were scanned, digitized. In the digitized images, the selected ROI was analysed with the Max-Mm Measure in one dimension. The two-dimensional extension measured the max-mm features in the ROI.
As the results, the threshold was increased, fewer extrema were detected and when the threshold exceeded the pixel density range of the image, no extrema were detected. Each image produced a characteristic curve which can be used to identify the texture. Each different density image of the same ROI in the base-material of the phantom produced a slightly different curve. Obviously, a larger size region of the ROT would give less variance in the features and more accurate classification results.
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