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.     

Detection of microcalcifications in digital mammograms usingwavelets

This paper presents an approach for detecting micro-calcifications in digital mammograms employing wavelet-based subband image decomposition. The microcalcifications appear in small clusters of few pixels with relatively high intensity compared with their neighboring pixels. These image features can be preserved by a detection system that employs a suitable image transform which can localize the signal characteristics in the original and the transform domain. Given that the microcalcifications correspond to high-frequency components of the image spectrum, detection of microcalcifications is achieved by decomposing the mammograms into different frequency subbands, suppressing the low-frequency subband, and, finally, reconstructing the mammogram from the subbands containing only high frequencies. Preliminary experiments indicate that further studies are needed to investigate the potential of wavelet-based subband image decomposition as a tool for detecting microcalcifications in digital mammograms     

Effects of JPEG2000 data compression on an automated system for detecting clustered microcalcifications in digital mammograms.

The functionalities of the JPEG2000 standard have led to its incorporation into digital imaging and communications in medicine (DICOM), which makes this compression method available for medical systems. In this study, we evaluated the compression of mammographic images with JPEG2000 (16 : 1, 20 : 1, 40 : 1, 60.4 : 1, 80: 1, and 106 : 1) for applications with a computer-aided detection (CAD) system for clusters of microcalcifications. Jackknife free-response receiver operating characteristic (JAFROC) analysis indicated that differences in the detection of clusters of microcalcifications were not statistically significant for uncompressed versus 16: 1 (T = -0.7780; p = 0.4370), 20 : 1 (T = 1.0361; p = 0.3007), and 40 : 1 (T = 1.6966; p = 0.0904); and statistically significant for uncompressed versus 60.4 : 1 (T = 5.8883; p < 0.008), 80 : 1 (T = 7.8414; p < 0.008), and 106 : 1 (T = 17.5034; p = < 0.008). Although there is a small difference in peak signal-to-noise ratio (PSNR) between compression ratios, the true-positive (TP) and false-positive (FP) rates, and the free-response receiver operating characteristic (FROC), figure of merit values considerably decreased from a 60 : 1 compression ratio. The performance of the CAD system is significantly reduced when using images compressed at ratios greater than 40 : 1 with JPEG2000 compared to uncompressed images. Mammographic images compressed up to 20 : 1 provide a percentage of correct detections by our CAD system similar to uncompressed images, regardless of the characteristics of the cluster. Further investigation is required to determine how JPEG2000 affects the detectability of clusters of microcalcifications as a function of their characteristics.     

Highly regular wavelets for the detection of clustered microcalcifications in mammograms

In this paper, we consider the problem of detecting clustered microcalcification in digitized mammograms using new wavelets with a high Sobolev regularity index. We experimentally assess the superiority of the new wavelets when compared with the classical ones.     

Segmentation algorithms for detecting microcalcifications inmammograms

The presence of microcalcification clusters in mammograms contributes evidence for the diagnosis of early stages of breast cancer. In many cases, microcalcifications are subtle and their detection can benefit from an automated system serving as a diagnostic aid. The potential contribution of such a system may become more significant as the number of mammograms screened increases to levels that challenge the capacity of radiology clinics. Many techniques for detecting microcalcifications start with a segmentation algorithm that indicates all candidate structures for the subsequent phases. Most algorithms used to segment microcalcifications have aspects that might raise operational difficulties, such as thresholds or windows that must be selected, or parametric models of the data. We present a new segmentation algorithm and compare it to two other algorithms: the multi-tolerance region-growing algorithm, which operates without the aspects mentioned above, and the active contour model, which has not been applied previously to segment microcalcifications. The new algorithm operates without threshold or window selection or parametric data models, and it is more than an order of magnitude faster than the other two     

Fractal modeling and segmentation for the enhancement of microcalcifications in digital mammograms

The objective of this research is to model the mammographic parenchymal and ductal patterns and enhance the microcalcifications using a deterministic fractal approach. According to the theory of deterministic fractal geometry, images can be modeled by deterministic fractal objects which are attractors of sets of two-dimensional (2-D) affine transformations. The iterated functions systems and the collage theorem are the mathematical foundations of fractal image modeling. Here, a methodology based on fractal image modeling is developed to analyze and model breast background structures. The authors show that general mammographic parenchymal and ductal patterns can be well modeled by a set of parameters of affine transformations. Therefore, microcalcifications can be enhanced by taking the difference between the original image and the modeled image. The authors' results are compared with those of the partial wavelet reconstruction and morphological operation approaches. The results demonstrate that the fractal modeling method is an effective way to enhance microcalcifications. It may also be able to improve the detection and classification of microcalcifications in a computer-aided diagnosis system.     

On techniques for detecting circumscribed masses in mammograms

A method for detecting one type of breast tumor, circumscribed masses, in mammograms is presented. It relies on a combination of criteria used by experts, including the shape, brightness contrast, and uniform density of tumor areas. The method uses modified median filtering to enhance mammogram images and template matching to detect the tumors. In the template matching step, suspicious areas are identified by thresholding the cross-correlation values, and a percentile method is used to determine a threshold for each film. In addition, two tests are used to remove false alarms from the resulting candidates. The results obtained by applying these techniques to a set of test images are described. They are judged encouraging.     

Detection of microcalcifications in mammograms using local maxima and adaptive wavelet transform analysis

A method for computer-aided diagnosis of microcalcification clusters in mammogram images is presented. Microcalcification clusters which are an early sign of breast cancer appear as isolated bright spots in mammograms. Therefore they correspond to local maxima of the image. The local maxima of the image is first detected and they are ranked according to a higher-order statistical test performed over the subband domain data.     

Wavelet transforms: an introduction

Wavelets and wavelet transforms are a relatively new topic in signal processing. Their development and, in particular, their application remains an active area of research. This paper presents a tutorial introduction to the theory, implementation and interpretation of the wavelet transform. The paper concentrates on the application of the wavelet transform to the time-scale (time-frequency) analysis of discrete signals. Examples are given of the analysis of basic test signals and of an actual electrocardiographic signal     

Wavelet transforms for vector fields using omnidirectionally balanced multiwavelets

Vector wavelet transforms for vector-valued fields can be implemented directly from multiwavelets; however, existing multiwavelets offer surprisingly poor performance for transforms in vector-valued signal-processing applications. In this paper, the reason for this performance failure is identified, and a remedy is proposed. A multiwavelet design criterion known as omnidirectional balancing is introduced to extend to vector transforms the balancing philosophy previously proposed for multiwavelet-based scalar-signal expansion. It is shown that the straightforward implementation of a vector wavelet transform, namely, the application of a scalar transform to each vector component independently, is a special case of an omnidirectionally balanced vector wavelet transform in which filter-coefficient matrices are constrained to be diagonal. Additionally, a family of symmetric-antisymmetric multiwavelets is designed according to the omnidirectional-balancing criterion. In empirical results for a vector-field compression system, it is observed that the performance of vector wavelet transforms derived from these omnidirectionally-balanced symmetric-antisymmetric multiwavelets is far superior to that of transforms implemented via other multiwavelets and can exceed that of diagonal transforms derived from popular scalar wavelets.     

图像小波变换VC程序实现

目前,小波理论应用已成为物理、数学和计算机等学科研究的热点,其中小波理论最成功的应用就是在图像处理中。在小波变换中,人们一般多运用MATLAB作为工具来进行图像处理的研究实现,但是在调用MATLAB小波工具箱的相关函数对小波进行构造时,往往忽略了对底层函数的理解,这对小波的进一步研究带来了不便。本设计重点阐述了二维离散小波变换的Mallat算法实现原理,并运用C＋＋编程实现,这改进了在MATLAB中不足之处,并且在MFC基础上进行了编程,得到一个可视化窗口,更加清晰地体现小波变换算法的核心。     

基于多尺度空间滤波和l1范数最近邻分类的乳腺图像微钙化点检测

钙化信息是乳腺癌早期诊断的一个重要依据,针对钙化点检测检出率较低和假阳性较高的问题,提出一种基于多尺度空间滤波和l1范数最近邻分类的乳腺图像微钙化点检测算法.首先利用多尺度空间滤波方法得到原图像的多尺度显著特征图,然后通过基于人眼视觉特性的钙化点分割方法得到粗检测钙化点的二值图像,并送入l1范数最近邻分类器去除假阳性点...     

Wavelet and subband transforms: fundamentals and communicationapplications

Subband and wavelet transforms have been a subject of great interest, especially in the fields of signal processing and applied mathematics. This article presents a tutorial on this subject, emphasizing the fundamentals and the reason for its success, importance, and potential. Subband and wavelet transform applications in communications are also reviewed. These application areas include interference excision, spread spectrum codes based on subband transform bases, CDMA, and discrete multitone transceivers     

基于空间屏蔽的M通道小波星图去噪法

小波图像去噪已经成为目前图像去噪的主要方法之一,在分析了小波变换的基本理论和小波变换的多尺度分析基础上,根据多尺度小波变换的多分辨特性,提出了M通道小波变换去噪方法;在该方法中,根据噪声信号小波变换的极大值随尺度的加大而显著减少的特点,将一种基于多尺度分析的空间屏蔽滤波法用于对小波系数进行处理.并将此方法用于星图降噪处理中,收到良好的效果.     

Performance of vibro-acoustography in detecting microcalcifications in excised human breast tissue: a study of 74 tissue samples

X-ray mammography is the principal modality used today for detection of breast microcalcifications and breast lesions associated with breast cancer. X-ray mammography, however, is ionizing and its sensitivity is greatly reduced in dense breasts. Hence, alternative noninvasive and nonionizing breast imaging tools that can aid physicians to better diagnose early-stage breast lesions are of great interest. Vibro-acoustography is a novel noninvasive imaging technique that uses ultrasound in a fundamentally new way. This method uses the radiation force of ultrasound to vibrate the tissue at low (kilohertz) frequency and records the resulting response to produce images that are related to the mechanical properties of the tissue. The goal of this study is to evaluate the performance of vibro-acoustography in detecting breast microcalcifications by conducting vibro-acoustography on 74 fixed breast tissue samples with known microcalcifications based on their radiographs. The results indicate that in most cases micro-calcifications can be detected by vibro-acoustography. Further development of vibro-acoustography may lead to a novel-imaging tool for in vivo detection of microcalcifications.     

Integrated wavelets for enhancement of microcalcifications in digital mammography

This paper presents a new algorithm for enhancement of microcalcifications in mammograms. The main novelty is the application of techniques we have developed for construction of filterbanks derived from the continuous wavelet transform. These discrete wavelet decompositions, called integrated wavelets, are optimally designed for enhancement of multiscale structures in images. Furthermore, we use a model based approach to refine existing methods for general enhancement of mammograms resulting in a more specific enhancement of microcalcifications. We present results of our method and compare them with known algorithms. Finally, we want to indicate how these techniques can also be applied to the detection of microcalcifications. Our algorithm was positively evaluated in a clinical study. It has been implemented in a mammography workstation designed for soft-copy reading of digital mammograms developed by IMAGETOOL, Germany.     

Fast mersenne number transforms for the computation of discrete fourier transforms

In this paper we present results on the computation of Discrete Fourier Transforms (DFT) using Mersenne Number Transforms (MNT). It is shown that in the case of Mersenne-composite Number Transforms, the number of multiplications per point for real input data is never more than one, even for sequence lengths exceeding one thousand points. The computation time per point for a length (2P + 1)-point DFT is simply equal to the time for one MNT multiplication and 3 MNT additions if a high-speed, parallel hardware module is used to implement the MNT unit. This new approach allows a large choice of wordlengths and in addition the control of data flow is extremely simple. We also present the results obtained by using Winograd's Fourier Transform Algorithm and the nested MNT to compute efficiently the DFT's of long sequences. We also show that the number of additions can be reduced significantly if Pseudo Mersenne-Number Transforms are used for the computation of DFTs.     

Convolution theorems for linear transforms

This correspondence explores the existence of convolution theorem for linear transformations under a variety of different assumptions. There are eight convolution theorems, all Fourier-related with only N operations in the transform domain and no ordering constraints on the convolution components in the result. They include circular convolutions and correlations     

Normalization of local contrast in mammograms

Equalizing image noise has been shown to be an important step in automatic detection of microcalcifications in digital mammograms. In this study, an accurate adaptive approach for noise equalization is presented and investigated. No additional information obtained from phantom recordings is involved in the method, which makes the approach robust and independent of film type and film development characteristics. Furthermore, it is possible to apply the method on direct digital mammograms as well. In this study, the adaptive approach is optimized by investigating a number of alternative approaches to estimate the image noise. The estimation of high-frequency noise as a function of the grayscale is improved by a new technique for dividing the grayscale in sample intervals and by using a model for additive high-frequency noise. It is shown that the adaptive noise equalization gives substantially better detection results than does a fixed noise equalization. A large database of 245 digitized mammograms with 341 clusters was used for evaluation of the method.