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

We present an approach to the detection of tumors in colonoscopic video. It is based on a new color feature extraction scheme to represent the different regions in the frame sequence. This scheme is built on the wavelet decomposition. The features named as color wavelet covariance (CWC) are based on the covariances of second-order textural measures and an optimum subset of them is proposed after the application of a selection algorithm. The proposed approach is supported by a linear discriminant analysis (LDA) procedure for the characterization of the image regions along the video frames. The whole methodology has been applied on real data sets of color colonoscopic videos. The performance in the detection of abnormal colonic regions corresponding to adenomatous polyps has been estimated high, reaching 97% specificity and 90% sensitivity.

/pdf/computer-aided-tumor-detection-in-endoscopic-video-using-4n5nqty54s.pdf

Computer-aided tumor detection in endoscopic video using color wavelet features

In this paper, a computer-aided diagnostic (CAD) system for the classification of hepatic lesions from computed tomography (CT) images is presented. Regions of interest (ROIs) taken from nonenhanced CT images of normal liver, hepatic cysts, hemangiomas, and hepatocellular carcinomas have been used as input to the system. The proposed system consists of two modules: the feature extraction and the classification modules. The feature extraction module calculates the average gray level and 48 texture characteristics, which are derived from the spatial gray-level co-occurrence matrices, obtained from the ROIs. The classifier module consists of three sequentially placed feed-forward neural networks (NNs). The first NN classifies into normal or pathological liver regions. The pathological liver regions are characterized by the second NN as cyst or "other disease". The third NN classifies "other disease" into hemangioma or hepatocellular carcinoma. Three feature selection techniques have been applied to each individual NN: the sequential forward selection, the sequential floating forward selection, and a genetic algorithm for feature selection. The comparative study of the above dimensionality reduction methods shows that genetic algorithms result in lower dimension feature vectors and improved classification performance.

A computer-aided diagnostic system to characterize CT focal liver lesions: design and optimization of a neural network classifier

The increasing use of biomarkers in cancer have led to the concept of personalized medicine for patients. Personalized medicine provides better diagnosis and treatment options available to clinicians. Radiological imaging techniques provide an opportunity to deliver unique data on different types of tissue. However, obtaining useful information from all radiological data is challenging in the era of ‘big data’. Recent advances in computational power and the use of genomics have generated a new area of research termed Radiomics. Radiomics is defined as the high throughput extraction of quantitative imaging features or texture (radiomics) from imaging to decode tissue pathology and creating a high dimensional data set for feature extraction. Radiomic features provide information about the gray-scale patterns, inter-pixel relationships. In addition, shape and spectral properties can be extracted within the same regions of interest on radiological images. Moreover, these features can be further used t...

Radiomics: a new application from established techniques

This paper presents a generalized statistical texture analysis technique for characterizing and recognizing typical, diagnostically most important, vascular patterns relating to cervical lesions from colposcopic images. The contributions of the research include: (1) the introduction of a generalized texture analysis technique based on the combination of the conventional statistical and structural textural analysis approaches by using a statistical description of geometric primitives; (2) the introduction of a set of textural measures that capture the specific characteristics of cervical textures as perceived by humans. An experimental study with real images demonstrated the feasibility and promise of the proposed approach in discriminating between cervical texture patterns indicative of different stages of cervical lesions.

/pdf/texture-analysis-for-classification-of-cervix-lesions-1nlzj1xdsk.pdf

Texture analysis for classification of cervix lesions

Ultrasound imaging is a powerful tool for characterizing the state of soft tissues; however, in some cases, where only subtle differences in images are seen as in certain liver lesions such as hemangioma and malignancy, existing B-scan methods are inadequate. More detailed analyses of image texture parameters along with artificial neural networks can be utilized to enhance differentiation. From B-scan ultrasound images, 11 texture parameters comprising of first, second and run length statistics have been obtained for normal, hemangioma and malignant livers. Tissue characterization was then performed using a multilayered backpropagation neural network. The results for 113 cases have been compared with a classification based on discriminant analysis. For linear discriminant analysis, classification accuracy is 79.6% and with neural networks the accuracy is 100%. The present results show that neural networks classify better than discriminant analysis, demonstrating a much potential for clinical application.

Application of artificial neural networks for the classification of liver lesions by image texture parameters

In-vivo differentiation of types of intraocular melanoma using texture parameters is reported. Differentiation of various classes of malignant melanoma through imaging is of special clinical interest for patient treatment and follow up. Texture parameters based on first and second order statistics and run length statistics of clinical ocular B-Scan images are utilized to obtain the quantitative information using image processing algorithms. The results are compared for tumors of different cases: spindle and mixed cell melanomas. Ultrasonic B-scan images were obtained using a commercial ultrasound scanner. Results show that the spindle cell melanoma has greater reflectivity and it is more homogeneous than the mixed cell melanoma and it is possible to differentiate the spindle and mixed cell melanoma using some of the texture parameters.

http://ieeexplore.ieee.org/iel3/3804/11132/00511726.pdf

H. Sujana

Papers

Application of artificial neural networks for the classification of liver lesions by image texture parameters

Ultrasonic image parameters for differentiating intraocular melanoma