Suspicious Region Detection and Identification Based on Intra-/Inter-Frame Analyses and Fuzzy Classifier for Breast Magnetic Resonance Imaging

Lin, Guo-Shiang; Chai, Sin-Kuo; Yeh, Wei-Cheng; Lin, Yi-Chang

doi:10.1142/s0218001414500074

Cited by 2 publications

(2 citation statements)

References 33 publications

(50 reference statements)

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“…There are many articles about ROI detection. In fact, the definition of ROI may change in different applications [1,2]. For instance, ROI may be the region containing car license plates, personal information, or the suspects in video surveillance applications.…”

Section: Introductionmentioning

confidence: 99%

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Vision-based patient identification recognition based on image content analysis and support vector machine for medical information system

Lin

Chai

et al. 2020

EURASIP J. Adv. Signal Process.

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In this paper, a vision-based patient identification recognition system based on image content analysis and support vector machine is proposed for medical information system, especially in dermatology. This proposed system is composed of three parts: pre-processing, candidate region detection, and digit recognition. To consider the efficiency of the proposed scheme, image normalization is performed. The color information is used to identify camera-captured screen images. In the pre-processing part, the effect of noise in captured screen images is reduced by a bilateral filter. The color and spatial information is used to initially and roughly locate the candidate region. To reduce the skew effect, a skew correction algorithm based on the Hough transform is developed. A template matching algorithm is used to find special symbols for locating the region of interest (ROI). For digit segmentation, digits are segmented in the ROI based on the vertical projection and adaptive thresholding. For the digit recognition, some features are measured from each digit segment and a classifier based on the support vector machine is applied to recognize digits.The experiment’s results show that the proposed system could effectively not only use color information to distinguish the captured screen images from the skin images but also detect the ROIs. After the digit segmentation, the accuracy rates of digit recognition are 98.4% and 94.2% for the proposed system and the Tesseract Optical Character Recognition (OCR) software, respectively. These results demonstrate that the proposed system outperforms the Tesseract OCR software in terms of the accuracy rate of digit recognition.

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Section: Introductionmentioning

confidence: 99%

“…In MIS, a ROI can be a region including the PII or tumors. In [1], the ROI containing tumors is located by removing the background part to raise the efficiency of tumor detection. As shown in captured screen images of Fig.…”

Section: Introductionmentioning

confidence: 99%