High risk human papillomavirus (HR-HPV) is major risk factor for uterine cervical cancer. There are approximately 15 types of HR-HPV. Liquid based cytology samples (116 samples) with high grade cervical lesions belonging to cervical intraepithelial neoplasia (CIN) 2, CIN 3, carcinoma in situ (CIS) and squamous cell carcinoma (SCC) were used after histologic confirmation. HR-HPV genotype assay was conducted using DNA chips. The HR-HPV infection rate was 81.9% with SCC samples showing the highest HR-HPV infection rate of 31%. CIN 3, CIS and CIN 2 showed infection rates of 25%, 16.4% and 9.5%, respectively. According to age with HR HPV infection rate, the 30~39 years-old group showed the highest infection rate by 92.3%. According to distribution with HR HPV genotyping, HPV 16 showed the highest infection rate by 42.3% whereas HPV 33 and HPV 58 showed infection rates of 11.7% and 10.8%, respectively. HPV 18 which is the second most common infected HPV genotype in the world showed 3.6%. Of the three most common oncogenic HR-HPV genotypes in
This paper proposes a new method that can recognize a sequence of hand motion expressing a sentence in sign language. Recognition procedure is divided into two steps: separation of the sequence of hand motions into the sub-sequences each of which expresses one word and combination of the words in order to construct a sentence having a meaning. In the first step, sequences of hand motion images are segmented by testing the continuity of the hand motions and by the multiscale image segmentation scheme. The trajectory of the hand motions are estimated by the affine transformation. Each sign in the sentence is represented by the extended chereme analysis model and each chereme is represented by the status vector for determining the transition in the HMM. In the second step, each sentence is also represented by the HMM. The Viterbi algorithm and context-dependent HMM are used to find the best state sequence in the HMM. The proposed algorithm has been tested with ten sequences of images, each of which expresses a sentence in Korean sign language. The experimental results have shown that the proposed algorithm can separate the sentence level image sequence into the word level sub-sequences with the success rate of 75% on average and recognize the sentence with the success rate of 80%.
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