[Presentation of the French translation of the Breast Imaging Reporting System and Data System (BI-RADS)]

Computerized Analysis of Mammogram Images for Early Detection of Breast Cancer

Breast cancer is widespread worldwide and can be cured if diagnosed early. Using digital mammogram images and image processing with artificial intelligence can play an essential role in breast cancer diagnosis. As many computerized algorithms for breast cancer diagnosis have significant limitations, such as noise handling and varying or low contrast in the images, it can be difficult to segment the abnormal region. These challenges could be overcome by proposing a new pre-processing model, exploring its impact on the post-processing module, and testing it on an extensive database. In this research work, the three-step method is proposed and validated on large databases of mammography images. The first step corresponded to the database classification, followed by the second step, which removed the pectoral muscle from the mammogram image. The third stage utilized new image-enhancement techniques and a new segmentation module to detect abnormal regions in a well-enhanced image to diagnose breast cancer. The pre-and post-processing modules are based on novel image processing techniques. The proposed method was tested using data collected from different hospitals in the Qassim Health Cluster, Qassim Province, Saudi Arabia. This database contained the five categories in the Breast Imaging and Reporting and Data System and consisted of 2892 images; the proposed method is analyzed using the publicly available Mammographic Image Analysis Society database, which contained 322 images. The proposed method gives good contrast enhancement with peak-signal to noise ratio improvement of 3 dB. The proposed method provides an accuracy of approximately 92% on 2892 images of Qassim Health Cluster, Qassim Province, Saudi Arabia. The proposed method gives approximately 97% on the Mammographic Image Analysis Society database. The novelty of the proposed work is that it could work on all Breast Imaging and Reporting and Data System categories. The performance of the proposed method demonstrated its ability to improve the diagnostic performance of the computerized breast cancer detection method.

Impact of Image Enhancement Module for Analysis of Mammogram Images for Diagnostics of Breast Cancer

Breast cancer is widespread around the world and can be cured if diagnosed at an early stage. Digital mammograms are used as the most effective imaging modalities for the diagnosis of breast cancer. However, mammography images suffer from low contrast, background noise as well as contrast as non-coherency among the regions, and these factors makes breast cancer diagnosis challenging. These problems can be overcome by using a new image enhancement technique. The objective of this research work is to enhance mammography images to improve the overall process of segmentation and classification of breast cancer diagnosis. We proposed the image enhancement for mammogram images, as well as the ablation of the pectoral muscle. The image enhancement technique involves several steps. In the first step, we process the mammography images in three channels (red, green and blue), the second step is based on the uniformity of the background on morphological operations, and the third step is to obtain a well-contrasted image using principal component analysis (PCA). The fourth step is based on the removal of the pectoral muscle using a seed-based region growth technique, and the last step contains the coherence of the different regions of the image using a second order Gaussian Laplacian (LoG) and an oriented diffusion filter to obtain a much-improved contrast image. The proposed image enhancement technique is tested with our data collected from different hospitals in Qassim health cluster Qassim province Saudi Arabia, and it contains the five Breast Imaging and Reporting System (BI-RADS) categories and this database contained 11,194 images (the images contain carnio-caudal (CC) view and mediolateral oblique(MLO) view of mammography images), and we used approximately 700 images to validate our database. We have achieved improved performance in terms of peak signal-to-noise ratio, contrast, and effective measurement of enhancement (EME) as well as our proposed image enhancement technique outperforms existing image enhancement methods. This performance of our proposed method demonstrates the ability to improve the diagnostic performance of the computerized breast cancer detection method.

An effective fine grading method of BI-RADS classification in mammography

PURPOSE

Mammography is an important imaging technique for the detection of early breast cancer. Doctors classify mammograms as Breast Imaging Reporting and Data Systems (BI-RADS). This study aims to provide an intelligent BI-RADS grading prediction method, which can help radiologists and clinicians to distinguish the most challenging 4A, 4B, and 4C cases in mammography.

METHODS

Firstly, the breast region, the lesion region, and the corresponding region in the contralateral breast were extracted. Four categories of features were extracted from the original images and the images after the wavelet transform. Secondly, an optimized sequential forward floating selection (SFFS) was used for feature selection. Finally, a two-layer classifier integration was employed for fine grading prediction. 45 cases from the hospital and 500 cases from Digital Database for Screening Mammography (DDSM) database were used for evaluation.

RESULTS

The classification performance of the support vector machine (SVM), Bayes, and random forest is very close on the 45 testing set, with the area under the receiver operating characteristic curve (AUC) of 0.978, 0.967, and 0.968. On the DDSM set, the AUC achieves 0.931, 0.938, and 0.874. Using the mean probability prediction, the AUC on the two datasets reaches 0.998 and 0.916. However, they are all significantly higher than the doctors' diagnosis, with the AUC of 0.807 and 0.725.

CONCLUSIONS

A BI-RADS fine grading (2, 3, 4A, 4B, 4C, 5) prediction model was proposed. Through the evaluation from different datasets, the performance is proved higher than that of the doctors, which may provide great help for clinical BI-RADS classification diagnosis. Therefore, our method can produce more effective and reliable results.

A New Computer-Aided Diagnosis System with Modified Genetic Feature Selection for BI-RADS Classification of Breast Masses in Mammograms

Mammography remains the most prevalent imaging tool for early breast cancer screening. The language used to describe abnormalities in mammographic reports is based on the Breast Imaging Reporting and Data System (BI-RADS). Assigning a correct BI-RADS category to each examined mammogram is a strenuous and challenging task for even experts. This paper proposes a new and effective computer-aided diagnosis (CAD) system to classify mammographic masses into four assessment categories in BI-RADS. The mass regions are first enhanced by means of histogram equalization and then semiautomatically segmented based on the region growing technique. A total of 130 handcrafted BI-RADS features are then extracted from the shape, margin, and density of each mass, together with the mass size and the patient's age, as mentioned in BI-RADS mammography. Then, a modified feature selection method based on the genetic algorithm (GA) is proposed to select the most clinically significant BI-RADS features. Finally, a back-propagation neural network (BPN) is employed for classification, and its accuracy is used as the fitness in GA. A set of 500 mammogram images from the digital database for screening mammography (DDSM) is used for evaluation. Our system achieves classification accuracy, positive predictive value, negative predictive value, and Matthews correlation coefficient of 84.5%, 84.4%, 94.8%, and 79.3%, respectively. To our best knowledge, this is the best current result for BI-RADS classification of breast masses in mammography, which makes the proposed system promising to support radiologists for deciding proper patient management based on the automatically assigned BI-RADS categories.

[Clinical practice guidelines: Benign breast tumor--Aims, methods and organization]

Conversely to breast cancer, few data and guidelines are available to explore and manage benign breast disorders. Therefore, the Collège national des gynécologues et obstétriciens français (CNGOF - French College of Gynaecologists and Obstetricians) decided to establish clinical practice guidelines for benign breast tumour (BBT). CNGOF appointed a committee with responsibility for selecting experts, compiling questions and summarizing the recommendations. The summary of valid scientific data for each question analyzed by the experts included a level of evidence, based on the quality of the data available and defined accordingly rating scheme developed by the Haute Autorité de santé (French National Authority for Health).

BI-RADS categorisation of 2,708 consecutive nonpalpable breast lesions in patients referred to a dedicated breast care unit

OBJECTIVES

To determine the malignancy rate of nonpalpable breast lesions, categorised according to the Breast Imaging Reporting and Data System (BI-RADS) classification in the setting of a Breast Care Unit.

METHODS

All nonpalpable breast lesions from consecutive patients referred to a dedicated Breast Care Unit were prospectively reviewed and classified into 5 BI-RADS assessment categories (0, 2, 3, 4, and 5).

RESULTS

A total of 2708 lesions were diagnosed by mammography (71.6%), ultrasound (8.7%), mammography and ultrasound (19.5%), or MRI (0.2%). The distribution of the lesions by BI-RADS category was: 152 in category 0 (5.6%), 56 in category 2 (2.1%), 742 in category 3 (27.4%), 1523 in category 4 (56.2%) and 235 in category 5 (8.7%). Histology revealed 570 malignant lesions (32.9%), 152 high-risk lesions (8.8%), and 1010 benign lesions (58.3%). Malignancy was detected in 17 (2.3%) category 3 lesions, 364 (23.9%) category 4 lesions and 185 (78.7%) category 5 lesions. Median follow-up was 36.9 months.

CONCLUSION

This pragmatic study reflects the assessment and management of breast impalpable abnormalities referred for care to a specialized Breast Unit. Multidisciplinary evaluation with BI-RADS classification accurately predicts malignancy, and reflects the quality of management. This assessment should be encouraged in community practice appraisal.

[Breast ultrasonography: an overview]

The most important roles of ultrasound in breast imaging include the diagnosis of cysts and the characterization of masses that have been incompletely assessed by mammography. Ultrasonography techniques such as harmonic and compound imaging have recently been introduced and are more efficient than conventional imaging in terms of lesion conspicuity and the characterization of breast nodule. The BI-RADS classification is an important system for describing and classifying breast lesions. With this approach, a population of benign solid breast lesions that does not require biopsy can be accurately defined. Ultrasonography should be performed as first-line examination in juvenile, in pregnant women and in patients with inflammatory syndrome. Ultrasound can detect mammographically occult breast the way they develop.