Microcalcifications In Mammograms Research Articles

Interactive Selective and Adaptive Clustering for Detection of Microcalcifications in Mammograms

Estevez, L., Kehtarnavaz, N., and Wendt, R. III, Interactive Selective and Adaptive Clustering for Detection of Microcalcifications in Mammograms,Digital Signal Processing6(1996), 224–232.This paper presents a clustering algorithm, called interactive selective and adaptive clustering (Isaac), to assist radiologists in looking for small clusters of microcalcifications in mammograms. Isaac is developed to identify suspicious microcalcification regions which are missed by other classification techniques due to false positive samples in the feature space. It comprises two parts: (i) selective clustering and (ii) interactive adaptation. The first part reduces the number of false positives by identifying the microcalcification subspace or domains in the feature space. The second part allows the radiologist to improve results by interactively identifying additional false positive or true negative samples. Clinical evaluations of mammograms indicate the potential of using this algorithm as an effective tool to bring microcalcification areas to the attention of the radiologist during a routine reading session of mammograms.

An improved computer-assisted diagnostic scheme using wavelet transform for detecting clustered microcalcifications in digital mammograms

We evaluated the potential usefulness of a computer-assisted diagnostic (CAD) scheme incorporating the wavelet transform for detecting clustered microcalcifications in mammograms. A wavelet transform technique was applied to the detection of clustered microcalcifications. We examined several wavelets to study their effectiveness in detecting subtle microcalcifications. We used a database consisting of 39 mammograms containing 41 clusters of microcalcifications. The performance of the wavelet-based CAD scheme was evaluated using free-response receiver operating characteristic analysis. The CAD scheme with the wavelet transform was useful in detecting some of the subtle microcalcifications that were not detected by our previous scheme, which was based on the difference-image technique. When the two schemes were combined, the overall performance was improved to a sensitivity of approximately 95%, with a false-positive rate of 1.5 clusters per image. The wavelet transform approach can improve the detection of subtle clustered microcalcifications.

Wavelet transforms for detecting microcalcifications in mammograms

Clusters of fine, granular microcalcifications in mammograms may be an early sign of disease. Individual grains are difficult to detect and segment due to size and shape variability and because the background mammogram texture is typically inhomogeneous. The authors develop a 2-stage method based on wavelet transforms for detecting and segmenting calcifications. The first stage is based on an undecimated wavelet transform, which is simply the conventional filter bank implementation without downsampling, so that the low-low (LL), low-high (LH), high-low (HL), and high-high (HH) sub-bands remain at full size. Detection takes place in HH and the combination LH+HL. Four octaves are computed with 2 inter-octave voices for finer scale resolution. By appropriate selection of the wavelet basis the detection of microcalcifications in the relevant size range can be nearly optimized. In fact, the filters which transform the input image into HH and LH+HL are closely related to prewhitening matched filters for detecting Gaussian objects (idealized microcalcifications) in 2 common forms of Markov (background) noise. The second stage is designed to overcome the limitations of the simplistic Gaussian assumption and provides an accurate segmentation of calcification boundaries. Detected pixel sites in HH and LH+HL are dilated then weighted before computing the inverse wavelet transform. Individual microcalcifications are greatly enhanced in the output image, to the point where straightforward thresholding can be applied to segment them. FROG curves are computed from tests using a freely distributed database of digitized mammograms.

A review of wavelets in biomedical applications

We present an overview of the various uses of the wavelet transform (WT) in medicine and biology. We start by describing the wavelet properties that are the most important for biomedical applications. In particular we provide an interpretation of the the continuous wavelet transform (CWT) as a prewhitening multiscale matched filter. We also briefly indicate the analogy between the WT and some of the the biological processing that occurs in the early components of the auditory and visual system. We then review the uses of the WT for the analysis of 1-D physiological signals obtained by phonocardiography, electrocardiography (ECG), mid electroencephalography (EEG), including evoked response potentials. Next, we provide a survey of wavelet developments in medical imaging. These include biomedical image processing algorithms (e.g., noise reduction, image enhancement, and detection of microcalcifications in mammograms), image reconstruction and acquisition schemes (tomography, and magnetic resonance imaging (MRI)), and multiresolution methods for the registration and statistical analysis of functional images of the brain (positron emission tomography (PET) and functional MRI (fMRI)). In each case, we provide the reader with same general background information and a brief explanation of how the methods work.

NASA high performance computing, communications, image processing, and data visualization-potential applications to medicine

High-speed information processing technologies being developed and applied by the Jet Propulsion Laboratory for NASA and Department of Defense mission needs have potential dual-uses in telemedicine and other medical applications. Fiber optic ground networks connected with microwave satellite links allow NASA to communicate with its astronauts in Earth orbit or on the moon, and with its deep space probes billions of miles away. These networks monitor the health of astronauts and or robotic spacecraft. Similar communications technology will also allow patients to communicate with doctors anywhere on Earth. NASA space missions have science as a major objective. Science sensors have become so sophisticated that they can take more data than our scientists can analyze by hand. High performance computers--workstations, supercomputer and massively parallel computers are being used to transform this data into knowledge. This is done using image processing, data visualization and other techniques to present the data--one's and zero's in forms that a human analyst can readily relate to and understand. Medical sensors have also explored in the in data output--witness CT scans, MRI, and ultrasound. This data must be presented in visual form and computers will allow routine combination of many two dimensional MRI images into three dimensional reconstructions of organs that then can be fully examined by physicians. Emerging technologies such as neural networks that are being "trained" to detect craters on planets or incoming missiles amongst decoys can be used to identify microcalcification in mammograms.

Automated Recognition of Micro Calcifications in Mammograms

Automated Recognition of Micro Calcifications in Mammograms

Segmentation of microcalcifications in mammograms

A systematic method for the detection and segmentation of microcalcifications in mammograms is presented. It is important to preserve size and shape of the individual calcifications as exactly as possible. A reliable diagnosis requires both rates of false positives as well as false negatives to be extremely low. The proposed approach uses a two-stage algorithm for spot detection and shape extraction. The first stage applies a weighted difference of Gaussians filter for the noise-invariant and size-specific detection of spots. A morphological filter reproduces the shape of the spots. The results of both filters are combined with a conditional thickening operation. The topology and the number of the spots are determined with the first filter, and the shape by means of the second. The algorithm is tested with a series of real mammograms, using identical parameter values for all images. The results are compared with the judgement of radiological experts, and they are very encouraging. The described approach opens up the possibility of a reproducible segmentation of microcalcifications, which is a necessary precondition for an efficient screening program.