Pattern recognition

Computer-aided detection of breast cancer on mammograms

Texture analysis is used in a very broad range of fields and applications, from texture classification (e.g., for remote sensing) to segmentation (e.g., in biomedical imaging), passing through image synthesis or pattern recognition (e.g., for image inpainting). For each of these image processing procedures, first, it is necessary to extract—from raw images—meaningful features that describe the texture properties. Various feature extraction methods have been proposed in the last decades. Each of them has its advantages and limitations: performances of some of them are not modified by translation, rotation, affine, and perspective transform; others have a low computational complexity; others, again, are easy to implement; and so on. This paper provides a comprehensive survey of the texture feature extraction methods. The latter are categorized into seven classes: statistical approaches, structural approaches, transform-based approaches, model-based approaches, graph-based approaches, learning-based approaches, and entropy-based approaches. For each method in these seven classes, we present the concept, the advantages, and the drawbacks and give examples of application. This survey allows us to identify two classes of methods that, particularly, deserve attention in the future, as their performances seem interesting, but their thorough study is not performed yet.

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Texture Feature Extraction Methods: A Survey

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

Breast cancer is the most common type of cancer among women in the western world. While mammography is regarded as the most effective tool for the detection and diagnosis of breast cancer, the interpretation of mammograms is a difficult and error-prone task. Hence, computer aids have been developed that assist the radiologist in the interpretation of mammograms. Computer-aided detection (CADe) systems address the problem that radiologists often miss signs of cancers that are retrospectively visible in mammograms. Furthermore, computer-aided diagnosis (CADx) systems have been proposed that assist the radiologist in the classification of mammographic lesions as benign or malignant. While a broad variety of approaches to both CADe and CADx systems have been published in the past two decades, an extensive survey of the state of the art is only available for CADe approaches. Therefore, a comprehensive review of the state of the art of CADx approaches is presented in this work. Besides providing a summary, the goals for this article are to identify relations, contradictions, and gaps in literature, and to suggest directions for future research. Because of the vast amount of publications on the topic, this survey is restricted to the two most important types of mammographic lesions: masses and clustered microcalcifications. Furthermore, it focuses on articles published in international journals.

CADx of mammographic masses and clustered microcalcifications: a review.

The current study investigates texture properties of the tissue surrounding microcalcification (MC) clusters on mammograms for breast cancer diagnosis. The case sample analyzed consists of 85 dense mammographic images, originating from the digital database for screening mammography. mammograms analyzed contain 100 subtle MC clusters (46 benign and 54 malignant). The tissue surrounding MCs is defined on original and wavelet decomposed images, based on a redundant discrete wavelet transform. Gray-level texture and wavelet coefficient texture features at three decomposition levels are extracted from surrounding tissue regions of interest (ST-ROIs). Specifically, gray-level first-order statistics, gray-level cooccurrence matrices features, and Lawspsila texture energy measures are extracted from original image ST-ROIs. Wavelet coefficient first-order statistics and wavelet coefficient cooccurrence matrices features are extracted from subimages ST-ROIs. The ability of each feature set in differentiating malignant from benign tissue is investigated using a probabilistic neural network. Classification outputs of most discriminating feature sets are combined using a majority voting rule. The proposed combined scheme achieved an area under receiver operating characteristic curve (Az) of 0.989. Results suggest that MCspsila ST texture analysis can contribute to computer-aided diagnosis of breast cancer.

Breast Cancer Diagnosis: Analyzing Texture of Tissue Surrounding Microcalcifications

Diagnosis of microcalcifications (MCs) is challenged by the presence of dense breast parenchyma, resulting in low specificity values and thus in unnecessary biopsies. The current study investigates whether texture properties of the tissue surrounding MCs can contribute to breast cancer diagnosis. A case sample of 100 biopsy-proved MC clusters (46 benign, 54 malignant) from 85 dense mammographic images, included in the Digital Database for Screening Mammography, was analysed. Regions of interest (ROIs) containing the MCs were pre-processed using a wavelet-based contrast enhancement method, followed by local thresholding to segment MCs; the segmented MCs were excluded from original image ROIs, and the remaining area (surrounding tissue) was subjected to texture analysis. Four categories of textural features (first order statistics, co-occurrence matrices features, run length matrices features and Laws' texture energy measures) were extracted from the surrounding tissue. The ability of each feature category ...

Texture analysis of tissue surrounding microcalcifications on mammograms for breast cancer diagnosis.

Assessing hip osteoarthritis severity utilizing a probabilistic neural network based classification scheme.

The aim of this study was to investigate the feasibility of texture analysis in characterizing endometrial tissue as depicted in two-dimensional (2D) grayscale transvaginal ultrasonography. Digital transvaginal ultrasound endometrial images were acquired from 65 perimenopausal and post-menopausal women prior to gynaecological operations; histology revealed 15 malignant and 50 benign cases. Images were processed with a wavelet-based contrast enhancement technique. Three regions of interest (ROIs) were identified (endometrium, endometrium plus adjacent myometrium, layer containing endometrial-myometrial interface) on each original and processed image. 32 textural features were extracted from each ROI employing first and second order statistics texture analysis algorithms. Textural features-based models were generated for differentiating benign from malignant endometrial tissue using stepwise logistic regression analysis. Models' performance was evaluated by means of receiver operating characteristic (ROC) analysis. The best logistic regression model comprised seven textural features extracted from the ROIs determined on the processed images; three features were extracted from the endometrium, while four features were extracted from the layer containing the endometrial-myometrial interface. The area under the ROC curve (Az) was 0.956i0.038, providing 86.0% specificity at 93.3% sensitivity using the cut-off level of 0.5 for probability of malignancy. Texture analysis of 2D grayscale transvaginal ultrasound images can effectively differentiate malignant from benign endometrial tissue and may contribute to computer-aided diagnosis of endometrial cancer.

Texture analysis of perimenopausal and post-menopausal endometrial tissue in grayscale transvaginal ultrasonography

A computer-aided classification system was developed for the assessment of the severity of hip osteoarthritis (OA) . Sixty-four radiographic images of normal and osteoarthritic hips were digitized and enhanced. Employing the Kellgren and Lawrence scale, the hips were grouped by three experienced orthopaedists into three OA-severity categories: Normal, Mild/Moderate and Severe. Utilizing custom-developed software, 64 ROIs corresponding to the radiographic Hip Joint Spaces were manually segmented and novel textural features were generated. These features were used in the design of a two-level classification scheme for characterizing hips as normal or osteoarthritic (1st level) and as of Mild/Moderate or Severe OA (2nd level). At each classification level, an ensemble of three classifiers was implemented. The proposed classification scheme discriminated correctly all normal hips from osteoarthritic hips (100% accuracy), while the discrimination accuracy between Mild/Moderate and Severe osteoarthritic hips was 95.7%. The proposed system could be used as a diagnosis decision-supporting tool.

I. Boniatis

Papers

Breast Cancer Diagnosis: Analyzing Texture of Tissue Surrounding Microcalcifications

Texture analysis of tissue surrounding microcalcifications on mammograms for breast cancer diagnosis.

Assessing hip osteoarthritis severity utilizing a probabilistic neural network based classification scheme.

Texture analysis of perimenopausal and post-menopausal endometrial tissue in grayscale transvaginal ultrasonography

Osteoarthritis severity of the hip by computer-aided grading of radiographic images.