This paper reviews ultrasound segmentation methods, in a broad sense, focusing on techniques developed for medical B-mode ultrasound images. First, we present a review of articles by clinical application to highlight the approaches that have been investigated and degree of validation that has been done in different clinical domains. Then, we present a classification of methodology in terms of use of prior information. We conclude by selecting ten papers which have presented original ideas that have demonstrated particular clinical usefulness or potential specific to the ultrasound segmentation problem

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Ultrasound image segmentation: a survey

Ultrasound (US) elasticity imaging is an extension of the ancient art of palpation and of earlier US methods for viewing tissue stiffness such as echopalpation. Elasticity images consist of either an image of strain in response to force or an image of estimated elastic modulus. There are 3 m

Imaging and estimation of tissue elasticity by ultrasound.

https://www.giejournal.org/article/S0016-5107(07)00006-5/pdf

EUS elastography of the pancreas: feasibility and pattern description of the normal pancreas, chronic pancreatitis, and focal pancreatic lesions

This paper presents the potential for fractal analysis of time sequence contrast-enhanced (CE) computed tomography (CT) images to differentiate between aggressive and nonaggressive malignant lung tumors (i.e., high and low metabolic tumors). The aim is to enhance CT tumor staging prediction accuracy through identifying malignant aggressiveness of lung tumors. As branching of blood vessels can be considered a fractal process, the research examines vascularized tumor regions that exhibit strong fractal characteristics. The analysis is performed after injecting 15 patients with a contrast agent and transforming at least 11 time sequence CE CT images from each patient to the fractal dimension and determining corresponding lacunarity. The fractal texture features were averaged over the tumor region and quantitative classification showed up to 83.3% accuracy in distinction between advanced (aggressive) and early-stage (nonaggressive) malignant tumors. Also, it showed strong correlation with corresponding lung tumor stage and standardized tumor uptake value of fluoro deoxyglucose as determined by positron emission tomography. These results indicate that fractal analysis of time sequence CE CT images of malignant lung tumors could provide additional information about likely tumor aggression that could potentially impact on clinical management decisions in choosing the appropriate treatment procedure.

Texture Analysis of Aggressive and Nonaggressive Lung Tumor CE CT Images

In this paper, we investigated the behavior of 22 co-occurrence statistics combined to six gray-scale quantization levels to classify breast lesions on ultrasound (BUS) images. The database of 436 BUS images used in this investigation was formed by 217 carcinoma and 219 benign lesions images. The region delimited by a minimum bounding rectangle around the lesion was employed to calculate the gray-level co-occurrence matrix (GLCM). Next, 22 co-occurrence statistics were computed regarding six quantization levels (8, 16, 32, 64, 128, and 256), four orientations (0° , 45° , 90° , and 135° ), and ten distances (1, 2,...,10 pixels). Also, to reduce feature space dimensionality, texture descriptors of the same distance were averaged over all orientations, which is a common practice in the literature. Thereafter, the feature space was ranked using mutual information technique with minimal-redundancy-maximal-relevance (mRMR) criterion. Fisher linear discriminant analysis (FLDA) was applied to assess the discrimination power of texture features, by adding the first m-ranked features to the classification procedure iteratively until all of them were considered. The area under ROC curve (AUC) was used as figure of merit to measure the performance of the classifier. It was observed that averaging texture descriptors of a same distance impacts negatively the classification performance, since the best AUC of 0.81 was achieved with 32 gray levels and 109 features. On the other hand, regarding the single texture features (i.e., without averaging procedure), the quantization level does not impact the discrimination power, since AUC=0.87 was obtained for the six quantization levels. Moreover, the number of features was reduced (between 17 and 24 features). The texture descriptors that contributed notably to distinguish breast lesions were contrast and correlation computed from GLCMs with orientation of 90° and distance more than five pixels.

Analysis of Co-Occurrence Texture Statistics as a Function of Gray-Level Quantization for Classifying Breast Ultrasound

Classification of breast ultrasound images using fractal feature.

Digital medical images are very easy to be modified for illegal purposes. For example, microcalcification in mammography is an important diagnostic clue, and it can be wiped off intentionally for insurance purposes or added intentionally into a normal mammography. In this paper, we proposed two methods to tamper detection and recovery for a medical image. A 1024 × 1024 x-ray mammogram was chosen to test the ability of tamper detection and recovery. At first, a medical image is divided into several blocks. For each block, an adaptive robust digital watermarking method combined with the modulo operation is used to hide both the authentication message and the recovery information. In the first method, each block is embedded with the authentication message and the recovery information of other blocks. Because the recovered block is too small and excessively compressed, the concept of region of interest (ROI) is introduced into the second method. If there are no tampered blocks, the original image can be obtained with only the stego image. When the ROI, such as microcalcification in mammography, is tampered with, an approximate image will be obtained from other blocks. From the experimental results, the proposed near-lossless method is proven to effectively detect a tampered medical image and recover the original ROI image. In this study, an adaptive robust digital watermarking method combined with the operation of modulo 256 was chosen to achieve information hiding and image authentication. With the proposal method, any random changes on the stego image will be detected in high probability.

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Tamper detection and recovery for medical images using near-lossless information hiding technique.

PURPOSE: To prospectively evaluate the accuracy of continuous ultrasonographic (US) images obtained during probe compression and computer-aided analysis for classification of biopsy-proved (reference standard) benign and malignant breast tumors. MATERIALS AND METHODS: This study was approved by the local ethics committee, and informed consent was obtained from all included patients. Serial US images of 100 solid breast masses (60 benign and 40 malignant tumors) were obtained with US probe compression in 86 patients (mean age, 45 years; range, 20–67 years). After segmentation of tumor contours with the level-set method, three features of strain on tissue from probe compression—contour difference, shift distance, area difference—and one feature of shape—solidity—were computed. A maximum margin classifier was used to classify the tumors by using these four features. The Student t test and receiver operating characteristic curve analysis were used for statistical analysis. RESULTS: The mean values of contour ...

Solid Breast Masses: Classification with Computer-aided Analysis of Continuous US Images Obtained with Probe Compression

Tissue elasticity of a lesion is a useful criterion for the diagnosis of breast ultrasound (US). Elastograms are created by comparing ultrasonic radio-frequency waveforms before and after a light-tissue compression. In this study, we evaluate the accuracy of continuous US strain image in the classification of benign from malignant breast tumors. A series of B-mode US images is applied and each case involves 60 continuous images obtained by using the steady artificial pressure of the US probe. In general, after compression by the US probe, a soft benign tumor will become flatter than a stiffened malignant tumor. We proposed a computer-aided diagnostic (CAD) system by utilizing the nonrigid image registration modality on the analysis of tumor deformation. Furthermore, we used some image preprocessing methods, which included the level set segmentation, to improve the performance. One-hundred pathology-proven cases, including 60 benign breast tumors and 40 malignant tumors, were used in the experiments to test the classification accuracy of the proposed method. Four characteristic values--normalized slope of metric value (NSM), normalized area difference (NAD), normalized standard deviation (NSD) and normalized center translation (NCT)--were computed for all cases. By using the support vector machine, the accuracy, sensitivity, specificity and positive and negative predictive values of the classification of continuous US strain images were satisfactory. The A(z) value of the support vector machine based on the four characteristic values used for the classification of solid breast tumors was 0.9358.

2-D ultrasound strain images for breast cancer diagnosis using nonrigid subregion registration

Sonographic evidence of tumor removal by Mammotome® excision does not confirm histological clearance. The operator finds it hard to determine if a malignant tumor has been fully removed, leaving a safe margin in the direction of each border; that is, the spatial orientation during tumor retrieval is not well-established by naked eye under sonographic guidance. We propose a computational imaging process to extract reasonable tumor contour in pre- and postoperative data sets for sonographic guidance so that Mammotome® excision can help the operator to evaluate the surgical outcome. There were five tumors in the study, including three benign and two malignant. The lesion of interest was delineated after 2-D examination was completed, then it was analyzed with 3-D breast ultrasound (US). To give a reference point for correlations between pre- and postoperative images, we used a marker tape pasted on the skin within the transducer scanning area and then the preoperative 3-D US images were obtained. Subsequently, 2-D breast US was applied during Mammotome® operation. After the Mammotome® procedures were finished, the postoperative 3-D US images were obtained; thus, we gained two different data sets of 3-D US images that were used for later analysis for evaluating the extension of postoperative margin status. From the results, the safe margin was not satisfactory in all directions, because the minimum differences measured by the proposed algorithm were not large enough in all five cases, and this was proved from two malignant mastectomy specimens. The experimental results representing this inadequate Mammotome® excision can be visualized through the computer aid. The comparison of tumor contour and excision margin may possibly be used for small malignant tumors in the future to improve the breast-conserving surgery. (E-mail: dlchen88@ms13.hinet.net)

Chii Jen Chen

Papers

Classification of breast ultrasound images using fractal feature.

Tamper detection and recovery for medical images using near-lossless information hiding technique.

Solid Breast Masses: Classification with Computer-aided Analysis of Continuous US Images Obtained with Probe Compression

2-D ultrasound strain images for breast cancer diagnosis using nonrigid subregion registration

Three-dimensional ultrasound in margin evaluation for breast tumor excision using Mammotome.