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.

A review of wavelets in biomedical applications

Computer-aided detection and classification of microcalcifications in mammograms: a survey

Clusters of microcalcifications in mammograms are an important early sign of breast cancer. This paper presents a computer-aided diagnosis (CAD) system for the automatic detection of clustered microcalcifications in digitized mammograms. The proposed system consists of two main steps. First, potential microcalcification pixels in the mammograms are segmented out by using mixed features consisting of wavelet features and gray level statistical features, and labeled into potential individual microcalcification objects by their spatial connectivity. Second, individual microcalcifications are detected by using a set of 31 features extracted from the potential individual microcalcification objects. The discriminatory power of these features is analyzed using general regression neural networks via sequential forward and sequential backward selection methods. The classifiers used in these two steps are both multilayer feedforward neural networks. The method is applied to a database of 40 mammograms (Nijmegen database) containing 105 clusters of microcalcifications. A free-response operating characteristics (FROC) curve is used to evaluate the performance. Results show that the proposed system gives quite satisfactory detection performance. In particular, a 90% mean true positive detection rate is achieved at the cost of 0.5 false positive per image when mixed features are used in the first step and 15 features selected by the sequential backward selection method are used in the second step. However, one must be cautious when interpreting the results, since the 20 training samples are also used in the testing step.

A CAD system for the automatic detection of clustered microcalcifications in digitized mammogram films

Breast cancer continues to be a significant public health problem in the United States. Approximately, 182,000 new cases of breast cancer are diagnosed and 46,000 women die of breast cancer each year. Even more disturbing is the fact that one out of eight women in the United States will develop breast cancer at some point during her lifetime. Since the cause of breast cancer remains unknown, primary prevention becomes impossible. Computer-aided mammography is an important and challenging task in automated diagnosis. It has great potential over traditional interpretation of film-screen mammography in terms of efficiency and accuracy. Microcalcifications are the earliest sign of breast carcinomas and their detection is one of the key issues for breast cancer control. In this study, a novel approach to microcalcification detection based on fuzzy logic technique is presented. Microcalcifications are first enhanced based on their brightness and nonuniformity. Then, the irrelevant breast structures are excluded by a curve detector. Finally, microcalcifications are located using an iterative threshold selection method. The shapes of microcalcifications are reconstructed and the isolated pixels are removed by employing the mathematical morphology technique. The essential idea of the proposed approach is to apply a fuzzified image of a mammogram to locate the suspicious regions and to interact the fuzzified image with the original image to preserve fidelity. The major advantage of the proposed method is its ability to detect microcalcifications even in very dense breast mammograms. A series of clinical mammograms are employed to test the proposed algorithm and the performance is evaluated by the free-response receiver operating characteristic curve. The experiments aptly show that the microcalcifications can be accurately detected even in very dense mammograms using the proposed approach.

A novel approach to microcalcification detection using fuzzy logic technique

https://www.cs.uoi.gr/~arly/papers/mamogramsAIM2005.pdf

Characterization of clustered microcalcifications in digitized mammograms using neural networks and support vector machines

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 inhomogeneous. We present a two-stage method based on wavelet transforms for detecting and segmenting calcifications. The first stage consists of a full resolution wavelet transform, which is simply the conventional filter bank implementation without downsampling, so that all sub-bands remain at full size. Four octaves are computed with two 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 the details sub-bands. Detected pixel sites in the LH, HL, and HH sub-bands are heavily weighted before computing the inverse wavelet transform. The LL component is omitted since gross spatial variations are of little interest. Individual microcalcifications are often greatly enhanced in the output image, to the point where straightforward thresholding can be applied to segment them. FROC curves are computed from tests using a well-known database of digitized mammograms. A true positive fraction of 85% is achieved at 0.5 false positives per image. >

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Wavelet transforms for detecting microcalcifications in mammography

We show that a biorthogonal spline wavelet closely approximates the pre-whitening matched filter for detecting microcalcifications in digitized mammograms. The wavelet transform acts like a bank of such detectors operating at multiple scales. Object shape computed from zero-crossings produced by the same wavelet helps to reduce false alarms. Detection performance is tested on 40 mammograms digitized at 100 /spl mu/m resolution.

Wavelet transform matched filters for the detection and classification of microcalcifications in mammography

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. We present a two- stage method based on wavelet transforms for detecting and segmenting calcifications. The first stage consists of a full resolution wavelet transform, which is simply the conventional filter bank implementation without downsampling, so that all sub-bands remain at full size. Four octaves are computed with two 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 the details sub-bands. In fact, the separable 2D filters which transform the input image into the HH details sub-bands are closely related to pre- whitening matched filters for detecting Gaussian objects (idealized microcalcifications) in Markov noise (background noise). The second stage is designed to overcome the limitations of the simplistic Gaussian assumption and provides a useful segmentation of calcifications boundaries. Detected pixel sites in the LH, HL, and HH sub-bands are heavily weighted before computing the inverse wavelet transform. The LL component is omitted since gross spatial variations are of little interest. Individual microcalcifications are often greatly enhanced in the output image, to the point where straightforward thresholding can be applied to segment them. FROC curves are computed from tests using a well-known database of digitized mammograms. A true positive fraction of 85% is achieved at 0.5 false positives per image.

Detection of microcalcifications in mammograms using wavelets

Computer-aided diagnosis techniques have been proposed as second opinion providers in digital mammography. This paper considers a new method of presenting CAD output to the radiologist. Instead of superimposing detected pixels or arrows on the mammogram, we adaptively enhance the most suspicious regions according to the weight indicated by the test statistic at the detector output. In so doing, CAD false positives promise to be less obtrusive to the viewer, and lesions missed by CAD (false negatives) may still be detected by the radiologist. In our method the entire mammogram is enhanced to some (spatially varying) degree. Enhancement is realized by applying nonlinear operators to wavelet coefficients computed at multiple scales. We combine this technique with the results of our previous wavelets-based CAD algorithm for detecting microcalcifications.© (1996) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Wavelet methods for combining CAD with enhancement of mammograms

We describe detectors capable of locating small tumors of variable size in the highly textured anatomic backgrounds typical of gamma-ray images. The problem of inhomogeneous background noise is solved using a spatially adaptive statistical scaling operation, which effectively pre-whitens the data and leads to a very simple form of adaptive matched filter. Detecting tumors of variable size is accomplished by processing the images formed in a Laplacian pyramid, each of which contains a narrower range of tumor scales. We compare the performance of this pyramid technique with our earlier nonlinear detector, which detects small tumors according to their signature in curvature feature space, where 'curvature' is the local curvature of the image data when viewed as a relief map. Computed curvature values are mapped to a normalized significance space using a windowed t-statistic. The resulting test statistic is thresholded at a chosen level of significance to give a positive detection. Nonuniform anatomic background activity is effectively suppressed. This curvature detector works quite well over a large range of tumor scales, although not as well as the pyramid/adaptive matched filter scheme. None of the multiscale techniques tested perform at the level of the fixed scale detectors. Tests are performed using simulated tumors superimposed on clinical gamma-ray images.

Hee Il Hahn

Papers

Wavelet transforms for detecting microcalcifications in mammography

Wavelet transform matched filters for the detection and classification of microcalcifications in mammography

Detection of microcalcifications in mammograms using wavelets

Wavelet methods for combining CAD with enhancement of mammograms

Tumor detection at multiple scales