Effects of image processing techniques on mammographic phantom images: a pilot study

Breast cancer is one of the most important diseases among females. According to the Malaysian Oncological Society (Wahid, 2007), about 4% of women who are 40 years old and above are suffering from breast cancer. Masses and microcalcifications are two important signs for breast cancer diagnosis on mammography. In this research, the effects of different image processing techniques which include enhancement, restoration, segmentation, and hybrid methods on phantom images were studied. Three different phantom images, which were obtained at 25kv (63.2 MAS), 28kv (29.8 MAS) and 35kv (9.5 MAS), were manipulated using image processing methods. The images were scored by two expert radiologists and the results were compared to explore any significant improvements. Meanwhile, the Wilcoxen Rank test was used to compare the quality of the manipulated images with the original one (alpha=0.05). Each image processing method was found to be effective on some particular criteria for image quality. Some methods were effective on just one criterion while some others were effective on a few criteria. The statistical test showed that there was an average improvement of 41 percent when the images were manipulated using the histogram modification methods. It could be concluded that different image processing methods have different effects on phantom images which generally improve radiologists’ visualization. The results confirm that the histogram stretching and histogram equation methods lead to higher improvement in image quality as compared to the original image (p < 0.05)

Detecting microcalcification clusters in digital mammograms: Study for inclusion into computer aided diagnostic prompting system

Among signs of breast cancer encountered in digital mammograms radiologists point to microcalcification clusters (MCCs). Their detection is a challenging problem from both medical and image processing point of views. This work presents two concurrent methods for MCC detection, and studies their possible inclusion to a computer aided diagnostic prompting system. One considers Wavelet Domain Hidden Markov Tree (WHMT) for modeling microcalcification edges. The model is used for differentiation between MC and non-MC edges based on the weighted maximum likelihood (WML) values. The classification of objects is carried out using spatial filters. The second method employs SUSAN edge detector in the spatial domain for mammogram segmentation. Classification of objects as calcifications is carried out using another set of spatial filters and Feedforward Neural Network (NN). A same distance filter is employed in both methods to find true clusters. The analysis of two methods is performed on 54 image regions from the mammograms selected randomly from DDSM database, including benign and cancerous cases as well as cases which can be classified as hard cases from both radiologists and the computer perspectives. WHMT/WML is able to detect 98.15% true positive (TP) MCCs under 1.85% of false positives (FP), whereas the SUSAN/NN method achieves 94.44% of TP at the cost of 1.85% for FP. The comparison of these two methods suggests WHMT/WML for the computer aided diagnostic prompting. It also certifies the low false positive rates for both methods, meaning less biopsy tests per patient

Maximum Energy Subsampling: A General Scheme For Multi-resolution Image Representation And Analysis

Image descriptors play an important role in image representation and analysis. Multi-resolution image descriptors can effectively characterize complex images and extract their hidden information. Wavelets descriptors have been widely used in multi-resolution image analysis. However, making the wavelets transform shift and rotation invariant produces redundancy and requires complex matching processes. As to other multi-resolution descriptors, they usually depend on other theories or information, such as filtering function, prior-domain knowledge, etc.; that not only increases the computation complexity, but also generates errors. We propose a novel multi-resolution scheme that is capable of transforming any kind of image descriptor into its multi-resolution structure with high computation accuracy and efficiency. Our multi-resolution scheme is based on sub-sampling an image into an odd-even image tree. Through applying image descriptors to the odd-even image tree, we get the relative multi-resolution image descriptors. Multi-resolution analysis is based on downsampling expansion with maximum energy extraction followed by upsampling reconstruction. Since the maximum energy usually retained in the lowest frequency coefficients; we do maximum energy extraction through keeping the lowest coefficients from each resolution level. Our multi-resolution scheme can analyze images recursively and effectively without introducing artifacts or changes to the original images, produce multi-resolution representations, obtain higher resolution images only using information from lower resolutions, compress data, filter noise, extract effective image features and be implemented in parallel processing