A Pc-Based Freehand Three-Dimensional Ultrasound.

Breast cancer is the number one killer disease among women in Malaysia. In the diagnosis of breast cancer, breast ultrasound examination is commonly used as a supplement to mammography

Prostate cancer recognition in ultrasound images

Our purpose is to aid medical doctors in prostate cancer detection via computer automated analysis of prostatic ultrasound imagery. Absorption of ultrasound signals is different in cancerous areas than in non-cancerous areas. The energy of the signal, the continuity of the signal, the autocorrelation function and frequency domain properties of prostatic ultrasound images are different in normal tissue than in cancerous tissue; This thesis presents an algorithm for automated cancer recognition in prostatic ultrasound imagery. Statistical and morphological based models are employed to classify regions of ultrasound imagery as either cancerous or non-cancerous. Application of our algorithm onto a limited set of cancerous and non-cancerous ultrasound images shows that our method has the ability to recognize cancer in cancerous ultrasound images. Misclassification occurs when cancerous tissue is classified as non-cancerous and noncancerous tissue is classified as cancerous. Occurrences of misclassification have been observed and investigated. (Abstract shortened by UMI.)

A noise detection, noise-motion separation and a cancer recognition theory and algorithm

In this thesis we describe a noise detection and a motion-noise separation algorithm, as well as the stochastic properties of the noise. The difference between corresponding pixels subject to one type of noise, of two frames, has mean vector equal to (0,0,0), and variance covariance matrix with relatively small variances, for the (R, G, B) difference values. The other type of noise is a result of disturbance of the light equilibrium due to motion in neighboring or nearby pixels. In this type of noise the mean of the difference is non-zero. Every pixel not included in the one type of noise or the other is part of the motion set between the two frames. The pixels are organized in macroblocks, so macroblocks containing pixels with motion are applied motion estimation and motion compensation methods first and subsequently the difference between the corresponding macroblocks of the two frames is obtained; This thesis furthermore describes an algorithm of cancer recognition of ultrasound images. (Abstract shortened by UMI.)