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This paper introduces a new computer-aided classification system for detection of prostate cancer in
Transrectal Ultrasound images (TRUS). To increase the efficiency of the computer aided classification
process, an intensity adjustment process is applied first, based on the Pulse Coupled Neural Network
(PCNN) with a median filter. This is followed by applying a PCNN-based segmentation algorithm to
detect the boundary of the prostate image. Combining the adjustment and segmentation enable to eliminate PCNN sensitivity to the setting of the various PCNN parameters whose optimal selection can be difficult and can vary even for the same problem. Then, wavelet based features have been extracted and
normalized, followed by application of a rough set analysis to discover the dependency between the
attributes and to generate a set of reduct that contains a minimal number of attributes. Finally, a rough
confusion matrix is designed that contain information about actual and predicted classifications done by a
classification system. Experimental results show that the introduced system is very successful and has high detection accuracy

This paper presents an efficient image registration technique using the Q-shift complex wavelet transform (Q-shift CWT). It is chosen for its key advantages compared to other wavelet transforms; such as shift invariance, directional selectivity, perfect reconstruction, limited redundancy and efficient computation. The experiments show that the proposed algorithm improves the computational efficiency and yields robust and consistent image registration compared with the classical wavelet transform.