Intensity-Invariant Texture Analysis for Classification of BI-RADS Category 3 Breast Masses

Application of computer-aided diagnosis to predict malignancy in BI-RADS 3 breast lesions

Purpose

To evaluate the ability of computer-aided diagnosis (CAD) system (S-Detect) to identify malignancy in ultrasound (US) -detected BI-RADS 3 breast lesions.

Materials and methods

148 patients with 148 breast lesions categorized as BI-RADS 3 were included in the study between January 2021 and September 2022. The malignancy rate, accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and area under the curve (AUC) were calculated.

Results

In this study, 143 breast lesions were found to be benign, and 5 breast lesions were malignant (malignancy rate, 3.4 %, 95 % confidence interval (CI): 0.5-6.3). The malignancy rate rose significantly to 18.2 % (4/22, 95 % CI: 2.1-34.3) in the high-risk group with a "possibly malignant" CAD result (p = 0.017). With a "possibly benign" CAD result, the malignancy rate decreased to 0.8 % (1/126, 95 % CI: 0-2.2) in the low-risk group (p = 0.297). The AUC, sensitivity, specificity, accuracy, PPV, and NPV of the CAD system in BI-RADS 3 breast lesions were 0.837 (95 % CI: 77.7-89.6), 80.0 % (95 % CI: 73.6-86.4), 87.4 % (95 % CI: 82.0-92.7), 87.2 % (95 % CI: 81.8-92.6), 18.2 % (95 % CI: 2.1-34.3) and 99.2 % (95 % CI: 97.8-100.0), respectively.

Conclusions

CAD system (S-Detect) enables radiologists to distinguish a high-risk group and a low-risk group among US-detected BI-RADS 3 breast lesions, so that patients in the low-risk group can receive follow-up without anxiety, while those in the high-risk group with a significantly increased malignancy rate should actively receive biopsy to avoid delayed diagnosis of breast cancer.

Deep Learning in Different Ultrasound Methods for Breast Cancer, from Diagnosis to Prognosis: Current Trends, Challenges, and an Analysis

Breast cancer is the second-leading cause of mortality among women around the world. Ultrasound (US) is one of the noninvasive imaging modalities used to diagnose breast lesions and monitor the prognosis of cancer patients. It has the highest sensitivity for diagnosing breast masses, but it shows increased false negativity due to its high operator dependency. Underserved areas do not have sufficient US expertise to diagnose breast lesions, resulting in delayed management of breast lesions. Deep learning neural networks may have the potential to facilitate early decision-making by physicians by rapidly yet accurately diagnosing and monitoring their prognosis. This article reviews the recent research trends on neural networks for breast mass ultrasound, including and beyond diagnosis. We discussed original research recently conducted to analyze which modes of ultrasound and which models have been used for which purposes, and where they show the best performance. Our analysis reveals that lesion classification showed the highest performance compared to those used for other purposes. We also found that fewer studies were performed for prognosis than diagnosis. We also discussed the limitations and future directions of ongoing research on neural networks for breast ultrasound.

A Comparison of Ultrasound Imaging Texture Analyses During the Early Postpartum With the Mode of Delivery

BACKGROUND

Breastfeeding is beneficial to infants. However, cesarean section is reported to be a risk factor for unsuccessful breastfeeding.

RESEARCH AIMS

(1) To extract discriminating data from texture analysis of breast ultrasound images in the immediate postpartum period; and (2) to compare the analysis results according to delivery mode.

METHODS

A cross-sectional, prospective non-experimental design with a questionnaire and observational components was used. Participants (N = 30) were women who delivered neonates at a center from September 2020 to December 2020. The participants underwent ultrasound examination of bilateral breasts 7-14 days after delivery. Ultrasound images were collected for texture analysis. A questionnaire about breastfeeding patterns was given to the participants on the day of the ultrasound examination.

RESULTS

No significant differences were found in texture analysis between the breasts of participants who had undergone Cesarean section and vaginal deliveries. The mean volume of total human milk produced in 1 day was significantly greater in the vaginal delivery group than in the cesarean section group (M = 350.87 ml, SD = 183.83 vs. M = 186.20 ml, SD = 184.02; p = .017). The pain score due to breast engorgement measured subjectively by participants was significantly lower in the vaginal delivery group than in the cesarean section group (M = 2.8, SD = 0.86 vs. M = 3.4, SD = 0.63; p = .047).

CONCLUSION

Texture analysis of breast ultrasound images did not demonstrate difference between the cesarean section and vaginal delivery groups in the immediate postpartum period; nevertheless, cesarean section was independently associated with less successful breastfeeding.

LRSCnet: Local Reference Semantic Code learning for breast tumor classification in ultrasound images

Purpose

This study proposed a novel Local Reference Semantic Code (LRSC) network for automatic breast ultrasound image classification with few labeled data.

Methods

In the proposed network, the local structure extractor is firstly developed to learn the local reference which describes common local characteristics of tumors. After that, a two-stage hierarchical encoder is developed to encode the local structures of lesion into the high-level semantic code. Based on the learned semantic code, the self-matching layer is proposed for the final classification.

Results

In the experiment, the proposed method outperformed traditional classification methods and AUC (Area Under Curve), ACC (Accuracy), Sen (Sensitivity), Spec (Specificity), PPV (Positive Predictive Values), and NPV(Negative Predictive Values) are 0.9540, 0.9776, 0.9629, 0.93, 0.9774 and 0.9090, respectively. In addition, the proposed method also improved matching speed.

Conclusions

LRSC-network is proposed for breast ultrasound images classification with few labeled data. In the proposed network, a two-stage hierarchical encoder is introduced to learn high-level semantic code. The learned code contains more effective high-level classification information and is simpler, leading to better generalization ability.

Assessment of the invariance and discriminant power of morphological features under geometric transformations for breast tumor classification

BACKGROUND AND OBJECTIVES

Computer-aided diagnosis (CAD) systems are intended to assist specialists in the interpretation of images aiming to support clinical conduct. In breast tumor classification, CAD systems involve a feature extraction stage, in which morphological features are used to describe the tumor shape. Such features are expected to satisfy at least two conditions: (1) discriminant to distinguish between benign and malignant tumors, and (2) invariant to geometric transformations. Herein, 39 morphological features were evaluated in terms of invariance and discriminant power for breast tumor classification.

METHODS

Morphological features were divided into region-based features, for describing the irregularity of the tumor shape, and boundary-based features, for measuring the anfractuosity of the tumor margin. Also, two datasets were considered in the experiments: 2054 breast ultrasound images and 892 mammographies. From both datasets, synthetic data augmentation was performed to obtain distinct combinations of rotation and scaling of breast tumors, from which morphological features were calculated. The linear discriminant analysis was used to classify breast tumors in benign and malignant classes. The area under the ROC curve (AUC) quantified the discriminant power of every morphological feature, whereas the relative difference (RD) between AUC values measured the invariance to geometric transformations. For indicating adequate performance, AUC and RD should tend toward unity and zero, respectively.

RESULTS

For both datasets, the convexity was the most discriminant feature that reached AUC > 0.81 with RD<1×10^-2, while the most invariant feature was the roundness that attained RD<1×10^-3 with AUC < 0.72. Additionally, for each dataset, the most discriminant and invariant features were combined for performing tumor classification. For mammography, it was achieved accuracy (ACC) of 0.76, sensitivity (SEN) of 0.76, and specificity (SPE) of 0.84, whereas for breast ultrasound the results were ACC=0.88,SEN=0.81, and SPE=0.91.

CONCLUSIONS

In general, region-based features are more discriminant and invariant than boundary-based features. Moreover, it was observed that an invariant feature is not necessarily a discriminant feature; hence, a balance between invariance and discriminant power should be attained for breast tumor classification.

Computer-aided diagnosis of isocitrate dehydrogenase genotypes in glioblastomas from radiomic patterns

World Health Organization tumor classifications of the central nervous system differentiate glioblastoma multiforme (GBM) into wild-type (WT) and mutant isocitrate dehydrogenase (IDH) genotypes. This study proposes a noninvasive computer-aided diagnosis to interpret the status of IDH in glioblastomas from transformed magnetic resonance imaging patterns. The collected image database was composed of 32 WT and 7 mutant IDH cases. For each image, a ranklet transformation which changed the original pixel values into relative coefficients was 1st applied to reduce the effects of different scanning parameters and machines on the underlying patterns. Extracting various textural features from the transformed ranklet images and combining them in a logistic regression classifier allowed an IDH prediction. We achieved an accuracy of 90%, a sensitivity of 57%, and a specificity of 97%. Four of the selected textural features in the classifier (homogeneity, difference entropy, information measure of correlation, and inverse difference normalized) were significant (P < .05), and the other 2 were close to being significant (P = .06). The proposed computer-aided diagnosis system based on radiomic textural features from ranklet-transformed images using relative rankings of pixel values as intensity-invariant coefficients is a promising noninvasive solution to provide recommendations about the IDH status in GBM across different healthcare institutions.

Intelligent Glioma Grading Based on Deep Transfer Learning of MRI Radiomic Features

According to a classification of central nervous system tumors by the World Health Organization, diffuse gliomas are classified into grade 2, 3, and 4 gliomas in accordance with their aggressiveness. To quantitatively evaluate a tumor’s malignancy from brain magnetic resonance imaging, this study proposed a computer-aided diagnosis (CAD) system based on a deep convolutional neural network (DCNN). Gliomas from a multi-center database (The Cancer Imaging Archive) composed of a total of 30 grade 2, 43 grade 3, and 57 grade 4 gliomas were used for the training and evaluation of the proposed CAD. Using transfer learning to fine-tune AlexNet, a DCNN, its internal layers, and parameters trained from a million images were transferred to learn how to differentiate the acquired gliomas. Data augmentation was also implemented to increase possible spatial and geometric variations for a better training model. The transferred DCNN achieved an accuracy of 97.9% with a standard deviation of ±1% and an area under the receiver operation characteristics curve (Az) of 0.9991 ± 0, which were superior to handcrafted image features, the DCNN without pretrained features, which only achieved a mean accuracy of 61.42% with a standard deviation of ±7% and a mean Az of 0.8222 ± 0.07, and the DCNN without data augmentation, which was the worst with a mean accuracy of 59.85% with a standard deviation ±16% and a mean Az of 0.7896 ± 0.18. The DCNN with pretrained features and data augmentation can accurately and efficiently classify grade 2, 3, and 4 gliomas. The high accuracy is promising in providing diagnostic suggestions to radiologists in the clinic.

Texture Analysis Based on Auto-Mutual Information for Classifying Breast Lesions with Ultrasound

Described here is a novel texture extraction method based on auto-mutual information (AMI) for classifying breast lesions. The objective is to extract discriminating information found in the non-linear relationship of textures in breast ultrasound (BUS) images. The AMI method performs three basic tasks: (i) it transforms the input image using the ranklet transform to handle intensity variations of BUS images acquired with distinct ultrasound scanners; (ii) it extracts the AMI-based texture features in the horizontal and vertical directions from each ranklet image; and (iii) it classifies the breast lesions into benign and malignant classes, in which a support-vector machine is used as the underlying classifier. The image data set is composed of 2050 BUS images consisting of 1347 benign and 703 malignant tumors. Additionally, nine commonly used texture extraction methods proposed in the literature for BUS analysis are compared with the AMI method. The bootstrap method, which considers 1000 bootstrap samples, is used to evaluate classification performance. The experimental results indicate that the proposed approach outperforms its counterparts in terms of area under the receiver operating characteristic curve, sensitivity, specificity and Matthews correlation coefficient, with values of 0.82, 0.80, 0.85 and 0.63, respectively. These results suggest that the AMI method is suitable for breast lesion classification systems.

Quantitative diagnosis of rotator cuff tears based on sonographic pattern recognition

The lifetime prevalence of shoulder pain is nearly 70% and is mostly attributable to subacromial disorders. A rotator cuff tear is the most severe form of subacromial disorders, and most occur in the supraspinatus. For clinical examination, shoulder ultrasound is recommended to detect supraspinatus tears. In this study, a computer-aided tear classification (CTC) system was developed to identify supraspinatus tears in ultrasound examinations and reduce inter-operator variability. The observed cases included 89 ultrasound images of supraspinatus tendinopathy and 102 of supraspinatus tear from 136 patients. For each case, intensity and texture features were extracted from the entire lesion and combined in a binary logistic regression classifier for lesion classification. The proposed CTC system achieved an accuracy rate of 92% (176/191) and an area under receiver operating characteristic curve (Az) of 0.9694. Based on its diagnostic performance, the CTC system has promise for clinical use.

Radiomics of US texture features in differential diagnosis between triple-negative breast cancer and fibroadenoma

Triple-negative breast cancer (TNBC) is sometimes mistaken for fibroadenoma due to its tendency to show benign morphology on breast ultrasound (US) albeit its aggressive nature. This study aims to develop a radiomics score based on US texture analysis for differential diagnosis between TNBC and fibroadenoma, and to evaluate its diagnostic performance compared with pathologic results. We retrospectively included 715 pathology-proven fibroadenomas and 186 pathology-proven TNBCs which were examined by three different US machines. We developed the radiomics score by using penalized logistic regression with a least absolute shrinkage and selection operator (LASSO) analysis from 730 extracted features consisting of 14 intensity-based features, 132 textural features and 584 wavelet-based features. The constructed radiomics score showed significant difference between fibroadenoma and TNBC for all three US machines (p < 0.001). Although the radiomics score showed dependency on the type of US machine, we developed more elaborate radiomics score for a subgroup in which US examinations were performed with iU22. This subsequent radiomics score also showed good diagnostic performance, even for BI-RADS category 3 or 4a lesions (AUC 0.782) which were presumed as probably benign or low suspicious of malignancy by radiologists. It was expected to assist radiologist’s diagnosis and reduce the number of invasive biopsies, although US standardization should be overcome before clinical application.

Classification of breast lesions in ultrasonography using sparse logistic regression and morphology-based texture features

PURPOSE

This work proposes a new reliable computer-aided diagnostic (CAD) system for the diagnosis of breast cancer from breast ultrasound (BUS) images. The system can be useful to reduce the number of biopsies and pathological tests, which are invasive, costly, and often unnecessary.

METHODS

The proposed CAD system classifies breast tumors into benign and malignant classes using morphological and textural features extracted from breast ultrasound (BUS) images. The images are first preprocessed to enhance the edges and filter the speckles. The tumor is then segmented semiautomatically using the watershed method. Having the tumor contour, a set of 855 features including 21 shape-based, 810 contour-based, and 24 textural features are extracted from each tumor. Then, a Bayesian Automatic Relevance Detection (ARD) mechanism is used for computing the discrimination power of different features and dimensionality reduction. Finally, a logistic regression classifier computed the posterior probabilities of malignant vs benign tumors using the reduced set of features.

RESULTS

A dataset of 104 BUS images of breast tumors, including 72 benign and 32 malignant tumors, was used for evaluation using an eightfold cross-validation. The algorithm outperformed six state-of-the-art methods for BUS image classification with large margins by achieving 97.12% accuracy, 93.75% sensitivity, and 98.61% specificity rates.

CONCLUSIONS

Using ARD, the proposed CAD system selects five new features for breast tumor classification and outperforms state-of-the-art, making a reliable and complementary tool to help clinicians diagnose breast cancer.

Machine learning techniques for breast cancer computer aided diagnosis using different image modalities: A systematic review

BACKGROUND AND OBJECTIVE

The high incidence of breast cancer in women has increased significantly in the recent years. Physician experience of diagnosing and detecting breast cancer can be assisted by using some computerized features extraction and classification algorithms. This paper presents the conduction and results of a systematic review (SR) that aims to investigate the state of the art regarding the computer aided diagnosis/detection (CAD) systems for breast cancer.

METHODS

The SR was conducted using a comprehensive selection of scientific databases as reference sources, allowing access to diverse publications in the field. The scientific databases used are Springer Link (SL), Science Direct (SD), IEEE Xplore Digital Library, and PubMed. Inclusion and exclusion criteria were defined and applied to each retrieved work to select those of interest. From 320 studies retrieved, 154 studies were included. However, the scope of this research is limited to scientific and academic works and excludes commercial interests.

RESULTS

This survey provides a general analysis of the current status of CAD systems according to the used image modalities and the machine learning based classifiers. Potential research studies have been discussed to create a more objective and efficient CAD systems.

A computer-aided diagnosis system for breast ultrasound based on weighted BI-RADS classes

BACKGROUND AND OBJECTIVE

Conventional computer-aided diagnosis (CAD) systems for breast ultrasound (BUS) are trained to classify pathological classes, that is, benign and malignant. However, from a clinical perspective, this kind of classification does not agree totally with radiologists' diagnoses. Usually, the tumors are assessed by using a BI-RADS (Breast Imaging-Reporting and Data System) category and, accordingly, a recommendation is emitted: annual study for category 2 (benign), six-month follow-up study for category 3 (probably benign), and biopsy for categories 4 and 5 (suspicious of malignancy). Hence, in this paper, a CAD system based on BI-RADS categories weighted by pathological information is presented. The goal is to increase the classification performance by reducing the common class imbalance found in pathological classes as well as to provide outcomes quite similar to radiologists' recommendations.

METHODS

The BUS dataset considers 781 benign lesions and 347 malignant tumors proven by biopsy. Moreover, every lesion is associated to one BI-RADS category in the set {2, 3, 4, 5}. Thus, the dataset is split into three weighted classes: benign, BI-RADS 2 in benign lesions; probably benign, BI-RADS 3 and 4 in benign lesions; and malignant, BI-RADS 4 and 5 in malignant lesions. Thereafter, a random forest (RF) classifier, denoted by RF_w, is trained to predict the weighted BI-RADS classes. In addition, for comparison purposes, a RF classifier is trained to predict pathological classes, denoted as RF_p.

RESULTS

The ability of the classifiers to predict the pathological classes is measured by the area under the ROC curve (AUC), sensitivity (SEN), and specificity (SPE). The RF_w classifier obtained AUC=0.872,SEN=0.826, and SPE=0.919, whereas the RF_p classifier reached AUC=0.868,SEN=0.808, and SPE=0.929. According to a one-way analysis of variance test, the RF_w classifier statistically outperforms (p < 0.001) the RF_p classifier in terms of the AUC and SEN. Moreover, the classification performance of RF_w to predict weighted BI-RADS classes is given by the Matthews correlation coefficient that obtained 0.614.

CONCLUSIONS

The division of the classification problem into three classes reduces the imbalance between benign and malignant classes; thus, the sensitivity is increased without degrading the specificity. Therefore, the CAD based on weighted BI-RADS classes improves the classification performance of the conventional CAD systems. Additionally, the proposed approach has the advantage of being capable of providing a multiclass outcome related to radiologists' recommendations.

Radiomic model for predicting mutations in the isocitrate dehydrogenase gene in glioblastomas

The present study proposed a computer-aided diagnosis system based on radiomic features extracted through magnetic resonance imaging to determine the isocitrate dehydrogenase status in glioblastomas. Magnetic resonance imaging data were obtained from 32 patients with wild-typeisocitrate dehydrogenase and 7 patients with mutant isocitrate dehydrogenase in glioblastomas. Radiomic features, namely morphological, intensity, and textural features, were extracted from the tumor area of each patient. The feature sets were evaluated using a logistic regression classifier to develop a prediction model. The accuracy of the global morphological and intensity features was 51% (20/39) and 59% (23/39), respectively. The textural features describing local patterns yielded an accuracy of 85% (33/39), which is significantly higher than that yielded by the morphological and intensity features. The agreement level (κ) between the prediction results and biopsy-proven pathology was 0.60. The proposed diagnosis system based on radiomic textural features shows promise for application in providing suggestions to radiologists for distinguishing isocitrate dehydrogenase mutations in glioblastomas.

The adaptive computer-aided diagnosis system based on tumor sizes for the classification of breast tumors detected at screening ultrasound

Screening ultrasound (US) is increasingly used as a supplement to mammography in women with dense breasts, and more than 80% of cancers detected by US alone are 1cm or smaller. An adaptive computer-aided diagnosis (CAD) system based on tumor size was proposed to classify breast tumors detected at screening US images using quantitative morphological and textural features. In the present study, a database containing 156 tumors (78 benign and 78 malignant) was separated into two subsets of different tumor sizes (<1cm and ⩾1cm) to explore the improvement in the performance of the CAD system. After adaptation, the accuracies, sensitivities, specificities and Az values of the CAD for the entire database increased from 73.1% (114/156), 73.1% (57/78), 73.1% (57/78), and 0.790 to 81.4% (127/156), 83.3% (65/78), 79.5% (62/78), and 0.852, respectively. In the data subset of tumors larger than 1cm, the performance improved from 66.2% (51/77), 68.3% (28/41), 63.9% (23/36), and 0.703 to 81.8% (63/77), 85.4% (35/41), 77.8% (28/36), and 0.855, respectively. The proposed CAD system can be helpful to classify breast tumors detected at screening US.

Quantitative glioma grading using transformed gray-scale invariant textures of MRI

BACKGROUND

A computer-aided diagnosis (CAD) system based on intensity-invariant magnetic resonance (MR) imaging features was proposed to grade gliomas for general application to various scanning systems and settings.

METHOD

In total, 34 glioblastomas and 73 lower-grade gliomas comprised the image database to evaluate the proposed CAD system. For each case, the local texture on MR images was transformed into a local binary pattern (LBP) which was intensity-invariant. From the LBP, quantitative image features, including the histogram moment and textures, were extracted and combined in a logistic regression classifier to establish a malignancy prediction model. The performance was compared to conventional texture features to demonstrate the improvement.

RESULTS

The performance of the CAD system based on LBP features achieved an accuracy of 93% (100/107), a sensitivity of 97% (33/34), a negative predictive value of 99% (67/68), and an area under the receiver operating characteristic curve (Az) of 0.94, which were significantly better than the conventional texture features: an accuracy of 84% (90/107), a sensitivity of 76% (26/34), a negative predictive value of 89% (64/72), and an Az of 0.89 with respective p values of 0.0303, 0.0122, 0.0201, and 0.0334.

CONCLUSIONS

More-robust texture features were extracted from MR images and combined into a significantly better CAD system for distinguishing glioblastomas from lower-grade gliomas. The proposed CAD system would be more practical in clinical use with various imaging systems and settings.

Effect of a computer-aided diagnosis system on radiologists' performance in grading gliomas with MRI

The effects of a computer-aided diagnosis (CAD) system based on quantitative intensity features with magnetic resonance (MR) imaging (MRI) were evaluated by examining radiologists' performance in grading gliomas. The acquired MRI database included 71 lower-grade gliomas and 34 glioblastomas. Quantitative image features were extracted from the tumor area and combined in a CAD system to generate a prediction model. The effect of the CAD system was evaluated in a two-stage procedure. First, a radiologist performed a conventional reading. A sequential second reading was determined with a malignancy estimation by the CAD system. Each MR image was regularly read by one radiologist out of a group of three radiologists. The CAD system achieved an accuracy of 87% (91/105), a sensitivity of 79% (27/34), a specificity of 90% (64/71), and an area under the receiver operating characteristic curve (Az) of 0.89. In the evaluation, the radiologists' Az values significantly improved from 0.81, 0.87, and 0.84 to 0.90, 0.90, and 0.88 with p = 0.0011, 0.0076, and 0.0167, respectively. Based on the MR image features, the proposed CAD system not only performed well in distinguishing glioblastomas from lower-grade gliomas but also provided suggestions about glioma grading to reinforce radiologists' confidence rating.

Computer-aided grading of gliomas based on local and global MRI features

BACKGROUND AND OBJECTIVES

A computer-aided diagnosis (CAD) system based on quantitative magnetic resonance imaging (MRI) features was developed to evaluate the malignancy of diffuse gliomas, which are central nervous system tumors.

METHODS

The acquired image database for the CAD performance evaluation was composed of 34 glioblastomas and 73 diffuse lower-grade gliomas. In each case, tissues enclosed in a delineated tumor area were analyzed according to their gray-scale intensities on MRI scans. Four histogram moment features describing the global gray-scale distributions of gliomas tissues and 14 textural features were used to interpret local correlations between adjacent pixel values. With a logistic regression model, the individual feature set and a combination of both feature sets were used to establish the malignancy prediction model.

RESULTS

Performances of the CAD system using global, local, and the combination of both image feature sets achieved accuracies of 76%, 83%, and 88%, respectively. Compared to global features, the combined features had significantly better accuracy (p = 0.0213). With respect to the pathology results, the CAD classification obtained substantial agreement κ = 0.698, p < 0.001.

CONCLUSIONS

Numerous proposed image features were significant in distinguishing glioblastomas from lower-grade gliomas. Combining them further into a malignancy prediction model would be promising in providing diagnostic suggestions for clinical use.

Computer-Aided Diagnosis of Different Rotator Cuff Lesions Using Shoulder Musculoskeletal Ultrasound

The lifetime prevalence of shoulder pain approaches 70%, which is mostly attributable to rotator cuff lesions such as inflammation, calcific tendinitis and tears. On clinical examination, shoulder ultrasound is recommended for the detection of lesions. However, there exists inter-operator variability in diagnostic accuracy because of differences in the experience and expertise of operators. In this study, a computer-aided diagnosis (CAD) system was developed to assist ultrasound operators in diagnosing rotator cuff lesions and to improve the practicality of ultrasound examination. The collected cases included 43 cases of inflammation, 30 cases of calcific tendinitis and 26 tears. For each case, the lesion area and texture features were extracted from the entire lesions and combined in a multinomial logistic regression classifier for lesion classification. The proposed CAD achieved an accuracy of 87.9%. The individual accuracy of this CAD system was 88.4% for inflammation, 83.3% for calcific tendinitis and 92.3% for tears. Cohen's k was 0.798. On the basis of its diagnostic performance, clinical use of this CAD technique has promise.