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Research on Medical Image Classification Based on the Feature of Wavelet Transform

Author: LiuBo
Tutor: SongYuQing
School: Jiangsu University
Course: Pattern Recognition and Intelligent Systems
Keywords: medical images texture analysis rotation invariant Gabor wavelet transform Support Vector Machine contributive factor pretreatment
CLC: TP391.41
Type: Master's thesis
Year: 2010
Downloads: 206
Quote: 2
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Abstract


With the coming of informative society, the way from which people get information is not to be limited in the number, the word and the symbol, but also from more and more kinds of images. Most of problems in image processing are feature extraction and recognition, such as image retrieval and classification, image compression and coding, image restoration or image reconstruction, target detection and recognition, edge extraction, image filtering, image segmentation and image signal separation and so on. Due to its diversity and complexity, image feature extraction is not only a hot issue but also the difficult one.On the basis of the in-depth analysis of medical image feature extraction algorithm home and abroad, this paper researched the texture feature extraction methods based on the Gabor wavelet. The main contents in this paper can be summarized as the following three aspects:(1)This paper summed up the method of medical image color feature extraction、texture feature extraction、shape feature extraction and semantic feature extraction fully. Some typical feature extraction methods such as gray-scale histograms feature, co-occurrence matrix feature, wavelet feature, invariant moment feature and clustering feature were analyzed.(2)The Gabor wavelet transform lacks in its ability to classify the medical CT image if it’s rotation invariant image. A new approach is Presented for rotation invariant medical texture classification based on Gabor wavelet transform.(3)In the process of the classification, Support Vector Machine(SVM)are employed to classify the texture patterns. A approach is presented to improve the efficiency of SVM using the way of adding pretreatment and contributive factor

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CLC: > Industrial Technology > Automation technology,computer technology > Computing technology,computer technology > Computer applications > Information processing (information processing) > Pattern Recognition and devices > Image recognition device
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