Improvement of digital mammogram images using histogram equalization, histogram stretching and median filter.

Breast cancer is one of the most important diseases in females worldwide. According to the Malaysian Oncological Society, about 4% of women who are 40 years old and above are involved have breast cancer. Masses and microcalcifications are two important signs of breast cancer diagnosis on mammography. Enhancement techniques, i.e. histogram equalization, histogram stretching and median filters, were used to provide better visualization for radiologists in order to help early detection of breast abnormalities. In this research 60 digital mammogram images which includes 20 normal and 40 confirmed diagnosed cancerous cases were selected and manipulated using the mentioned techniques. The original and manipulated images were scored by three expert radiologists. Results showed that the selected methods have a positive significant effect on image quality.

Wavelet-based compression of medical images: filter-bank selection and evaluation.

Wavelet-based image coding algorithms (lossy and lossless) use a fixed perfect reconstruction filter-bank built into the algorithm for coding and decoding of images. However, no systematic study has been performed to evaluate the coding performance of wavelet filters on medical images. We evaluated the best types of filters suitable for medical images in providing low bit rate and low computational complexity. In this study a variety of wavelet filters are used to compress and decompress computed tomography (CT) brain and abdomen images. We applied two-dimensional wavelet decomposition, quantization and reconstruction using several families of filter banks to a set of CT images. Discreet Wavelet Transform (DWT), which provides efficient framework of multi-resolution frequency was used. Compression was accomplished by applying threshold values to the wavelet coefficients. The statistical indices such as mean square error (MSE), maximum absolute error (MAE) and peak signal-to-noise ratio (PSNR) were used to quantify the effect of wavelet compression of selected images. The code was written using the wavelet and image processing toolbox of the MATLAB (version 6.1). This results show that no specific wavelet filter performs uniformly better than others except for the case of Daubechies and bi-orthogonal filters which are the best among all. MAE values achieved by these filters were 5 x 10(-14) to 12 x 10(-14) for both CT brain and abdomen images at different decomposition levels. This indicated that using these filters a very small error (approximately 7 x 10(-14)) can be achieved between original and the filtered image. The PSNR values obtained were higher for the brain than the abdomen images. For both the lossy and lossless compression, the 'most appropriate' wavelet filter should be chosen adaptively depending on the statistical properties of the image being coded to achieve higher compression ratio.

Congenital breast asymmetry: subjective and objective assessment.

There is currently no standard objective method for the assessment of developmental breast asymmetry. The results of corrective surgery in 24 patients with congenital breast deformities were evaluated subjectively (symmetry scores by patients and panels of observers), and objectively (linear measurements of nipple position and stereophotogrammetrically determined breast volumes). The latter were obtained using a newly developed prototype computer based technique (Bodymap). This study is the first reported use of stereophotogrammetry in assessing results of surgery for the correction of congenital breast asymmetry. The results obtained, their clinical implications, and the usefulness of Bodymap in the objective measurement of breast asymmetry are discussed.

Bodymap: an image processing system for the measurement of body surface profiles encountered in skin expansion surgery.

A personal computer based image processing system for the mapping and subsequent analysis of local areas of the human body requiring no more than 180 degrees of vision is described. The operations of image capture and data extraction are undertaken in real time and photogrammetric reconstruction takes typically a further 30 s. Accuracy of the system has been assessed by photogrammetrically reconstructing 37 points whose coordinates had been precisely established by traditional survey techniques. An average discrepancy between surveyed and reconstructed positions of 0.485 mm was obtained; this level of accuracy compares highly favourably with results achieved using alternative methods and which do not have the level of automation inherent in the described system. The system has been developed specifically to provide quantitative values for parameters of particular interest in skin expansion surgery: consequently the post-reconstruction facilities available in this initial system are surface area and enclosed volume calculations and shaded surface display. Further development work currently in progress is also briefly discussed.