| With the development of medical imaging modalities and computer technology,doctors can provide higher and higher quality of service and release the patients fromsuffering of disease. Unfortunately, for the indiscoverableness of diseases and thelimitations of treatment level, the misinformation is still very high, which makespatients losing the optimal opportunity of treatment and leading high cost and low effect.How to reduce the misinformation and improve the correct diagnosis rate is becoming achallenge for modality producers, radiology doctors, especially experts of digital imageprocessing.Under such background, we do some helpful tries on multi-scale medical imageenhancement and hierarchical focus detection. The former is used to mine visualinformation for doctors as much as possible and to help the doctor mastering the truesituation of the patients. With CR/DR as examples, a wavelet-based multi-scale imageenhancement algorithm with pyramid structure is proposed and implemented in VC++.The later makes the automatic detection of focuses of mammary cancer as target,including mass regions and micro-calcifications. Based on the theory of hierarchical,rough sets, fuzzy sets and multi-tolerance region growing, mass region andmicro-calcifications detection algorithms are presented respectively in this thesis. Bymachine learning on the experience of doctors, the computer can automatically label thedoubtful focuses regions. Then doctors can interactively analyze the mammary imageswith the aid of computer and make the final decision of diagnosis. With the computeraided detection (CAD), doctor can improve the correct diagnosis rate further.The simulation results and effects illustrate the practicability and robustness of theproposed algorithms Multi-Scale Medical Image Enhancement and Hierarchical FocusDetection Algorithms can enhance the visual effect of medical images and the detectionrate of mammary disease with good practical application values.
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