Computer-aided Diagnosis in Breast Ultrasound

Cancer remains a leading cause of death in Taiwan, and the prevalence of breast cancer has increased in recent years. The early detection and diagnosis of breast cancer is the key to ensuring prompt treatment and a reduced death rate. Mammography and ultrasound (US) are the main imaging techniques used in the detection of breast cancer. The heterogeneity of breast cancers leads to an overlap in benign and malignant ultrasonography images, and US examinations are also operator dependent. Recently, computer-aided diagnosis (CAD) has become a major research topic in medical imaging and diagnosis. Technical advances such as tissue harmonic imaging, compound imaging, split screen imaging and extended field-of-view imaging, Doppler US, the use of intravenous contrast agents, elastography, and CAD systems have expanded the clinical application of breast US. Breast US CAD can be an efficient computerized model to provide a second opinion and avoid interobserver variation. Various breast US CAD systems have been developed using techniques which combine image texture extraction and a decision-making algorithm. However, the textural analysis is system dependent and can only be performed well using one specific US system. Recently, several researchers have demonstrated the use of such CAD systems with various US machines mainly for preprocessing techniques designed to homogenize textural features between systems. Morphology-based CAD systems used for the diagnosis of solid breast tumors have the advantage of being nearly independent of either the settings of US systems or different US machines. Future research on CAD systems should include pathologically specific tissue-related and hormonerelated conjecture, which could be applied to picture archiving and communication systems or teleradiology

Comparative assessment of texture features for the identification of cancer in ultrasound images: a review

In this paper, we review the use of texture features for cancer detection in Ultrasound (US) images of breast, prostate, thyroid, ovaries and liver for Computer-Aided Diagnosis (CAD) systems. This paper shows that texture features are a valuable tool to extract diagnostically relevant information from US images. This information helps practitioners to discriminate normal from abnormal tissues. A drawback of some classes of texture features comes from their sensitivity to both changes in image resolution and grayscale levels. These limitations pose a considerable challenge to CAD systems, because the information content of a specific texture feature depends on the US imaging system and its setup. Our review shows that single classes of texture features are insufficient, if considered alone, to create robust CAD systems, which can help to solve practical problems, such as cancer screening. Therefore, we recommend that the CAD system design involves testing a wide range of texture features along with features obtained with other image processing methods. Having such a competitive testing phase helps the designer to select the best feature combination for a particular problem. This approach will lead to practical US based cancer detection systems which de- liver real benefits to patients by improving the diagnosis accuracy while reducing health care cost

Automatic Detection and Classification of Breast Tumors in Ultrasonic Images Using Texture and Morphological Features

Due to severe presence of speckle noise, poor image contrast and irregular lesion shape, it is challenging to build a fully automatic detection and classification system for breast ultrasonic images. In this paper, a novel and effective computer-aided method including generation of a region of interest (ROI), segmentation and classification of breast tumor is proposed without any manual intervention. By incorporating local features of texture and position, a ROI is firstly detected using a self-organizing map neural network. Then a modified Normalized Cut approach considering the weighted neighborhood gray values is proposed to partition the ROI into clusters and get the initial boundary. In addition, a regional-fitting active contour model is used to adjust the few inaccurate initial boundaries for the final segmentation. Finally, three textures and five morphologic features are extracted from each breast tumor; whereby a highly efficient Affinity Propagation clustering is used to fulfill the malignancy and benign classification for an existing database without any training process. The proposed system is validated by 132 cases (67 benignancies and 65 malignancies) with its performance compared to traditional methods such as level set segmentation, artificial neural network classifiers, and so forth. Experiment results show that the proposed system, which needs no training procedure or manual interference, performs best in detection and classification of ultrasonic breast tumors, while having the lowest computation complexity

Comparison between A-mode and B-mode ultrasound in local hyperthermia monitoring

Hyperthermia therapy is one of the therapy methods used for cancer treatment. It has shown to be an effective way of treating the cancerous tissue when compared to surgery, chemotherapy and radiation. However, real time monitoring method is capable in delivering a consistent heat and preventing any damages to the nearby tissue. Ultrasound is among the widely used technique in clinical setting. A-Mode ultrasound involves one-dimensional (1D) signal processing which enables a quantitative measurement on different types of breast tissues to be conducted faster as it has relatively simple signal processing requirement. On the other hand, B-Mode ultrasound offers good spatial resolution for thermal monitoring. Therefore, the aim of this study is to investigate and to compare the most optimum A-Mode and B-Mode ultrasound parameters to monitor hyperthermia in normal and pathological breast tissue. A series of experiment was conducted on 40 female Sprague Dawley rats. The pathological and normal rats were dissected and exposed to hyperthermia at variation temperature of 37oC (body temperature) and 40oC, 45oC, 50oC and 55oC for hyperthermia temperatures. A-Mode and B-Mode of 7.5 Mhz and 6Mhz was used simultaneously during the experiment for collecting acoustic information and scanning purposes before and after the hyperthermia exposure. Result obtained shows that, for normal tissue condition of both A-Mode and B-Mode, the attenuation calculation to mean of pixel intensity found to be (3.59±0.04)dB and 187.68 at temperature value of 50 oC. Meanwhile, in pathological tissue condition, the attenuation value with respect to pixel intensity was obtained by (3.36±0.26)dB at temperature value of 45oC and 199.26 was achieved at temperature value of 40oC. For backscatter coefficient to variance analysis, the result found that, in both A-Mode and B-Mode normal tissue condition, at temperature value of 40oC, (1.81±0.25) of backscatter coefficient was obtained while at 45oC, the variance value of 3298.94 was achieved. In pathological tissue, the temperature value of 40oC and 55oC was the most pronounce temperature dependent of (1.45±0.28) for backscatter coefficient with respect to 3275.35 of variance analysis. The result obtained from artificial neural network have shown that, 91.67% to 87.5% of testing to validation percentage accuracy of A-Mode was achieved, while in B-Mode, 88.89% and 81.25% of testing and validation data was obtained. Therefore, it is shown that, the use of A-Mode with comparison to B-Mode ultrasound can be used as another potential approach since its attenuation to pixel intensity and backscatter coefficient with respect to variance of A-Mode and B-Mode is very sensitive to the tissue structure in monitoring hyperthermia therapy with respect to the changes of temperature

A Two-Step Segmentation Method for Breast Ultrasound Masses Based on Multi-resolution Analysis

Breast ultrasound images have several attractive properties that make them an interesting tool in breast cancer detection. However, their intrinsic high noise rate and low contrast turn mass detection and segmentation into a challenging task. In this article, a fully automated two-stage breast mass segmentation approach is proposed. In the initial stage, ultrasound images are segmented using support vector machine or discriminant analysis pixel classification with a multiresolution pixel descriptor. The features are extracted using non-linear diffusion, bandpass filtering and scale-variant mean curvature measures. A set of heuristic rules complement the initial segmentation stage, selecting the region of interest in a fully automated manner. In the second segmentation stage, refined segmentation of the area retrieved in the first stage is attempted, using two different techniques. The AdaBoost algorithm uses a descriptor based on scale-variant curvature measures and non-linear diffusion of the original image at lower scales, to improve the spatial accuracy of the ROI. Active contours use the segmentation results from the first stage as initial contours. Results for both proposed segmentation paths were promising, with normalized Dice similarity coefficients of 0.824 for AdaBoost and 0.813 for active contours. Recall rates were 79.6% for AdaBoost and 77.8% for active contours, whereas the precision rate was 89.3% for both methods.info:eu-repo/semantics/publishedVersio