Classification of Breast Ultrasound Tomography by Using Textural Analysis

Background: Ultrasound imaging has become one of the most widely utilized adjunct tools in breast cancer screening due to its advantages. The computer-aided detection of breast ultrasound is rapid development via significant features extracted from images. Objectives: The main aim was to identify features of breast ultrasound image that can facilitate reasonable classification of ultrasound images between malignant and benign lesions. Patients and Methods: This research was a retrospective study in which 85 cases (35 malignant [positive group] and 50 benign [negative group] with diagnostic reports) with ultrasound images were collected. The B-mode ultrasound images have manually selected regions of interest (ROI) for estimated features of an image. Then, a fractal dimensional (FD) image was generated from the original ROI by using the box-counting method. Both FD and ROI images were extracted features, including mean, standard deviation, skewness, and kurtosis. These extracted features were tested as significant by t-test, receiver operating characteristic (ROC) analysis and Kappa coefficient. Results: The statistical analysis revealed that the mean texture of images performed the best in differentiating benign versus malignant tumors. As determined by the ROC analysis, the appropriate qualitative values for the mean and the LR model were 0.85 and 0.5, respectively. The sensitivity, specificity, accuracy, positive predicted value (PPV), negative predicted value (NPV), and Kappa for the mean was 0.77, 0.84, 0.81, 0.77, 0.84, and 0.61, respectively. Conclusion: The presented method was efficient in classifying malignant and benign tumors using image textures. Future studies on breast ultrasound texture analysis could focus on investigations of edge detection, texture estimation, classification models, and image features.

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Lesion Segmentation in Breast Ultrasound Images using Optimized Marked Watershed Method

10.21203/rs.3.rs-137797/v1 ◽

2020 ◽

Author(s):

Xiaoyan Shen ◽

He Ma ◽

Ruibo Liu ◽

Hong Li ◽

Jiachuan He ◽

...

Keyword(s):

Malignant Tumors ◽

Classical Method ◽

Ultrasound Image ◽

Breast Ultrasound ◽

Experimental Result ◽

Benign Tumors ◽

Ultrasound Images ◽

Lesion Segmentation ◽

Obvious Effect ◽

Side Window

Abstract Background: Ultrasound is the most popular tool for early detection of breast cancer because of its non radiation and low cost. However, breast ultrasound(BUS) images have low resolution and speckle noise, which make lesion segmentation become a challenge. Most of deep learning(DL) models applied on images segmentation don't have good generalization ability for BUS images. Therefore, it is time to go back to the classical method and consider combining it with DL to achieve more accurate and efficient effect in a semi-automatic way.Methods: This paper mainly proposed an effective and efficient semi-automatic BUS images segmentation method, Adaptive morphological snake and marked watershed( AMSMW). It includes two parts: preprocessing and segmentation. In the first part, we combine contrast limited adaptive histogram equalization(CLAHE) and side window filtering(SWF) methods for the first time. In the second part, We use the proposed adaptive morphological snake algorithm (AMS) to provide a mark for marked watershed(MW) method. Results: we tested on 500 BUS images, whose ratio of benign and malignant is 1:1. After quantitative and qualitative analysis, AMSMW is proven to outperform existing classical methods on the effectiveness and efficiency. Furthermore, we compared with Zhuang’s RDAU-NET on both our dataset and theirs. Experimental result showes AMSMW achieved better performance on most of indicators, including loss, accuracy, sensitivity, dice and F1-score. Conlusions: The new image preprocessing method proposed by us has obvious effect on segmentation of breast ultrasound image. In addition, the proposed adaptive morphology snake method and optimized marked watershed turn out to be more efficient and effective than some relative classical method and the advanced DL method at present. Moreover, by studying on the algorithm’s sensitivity in segmenting benign and malignant tumors, we found that AMSMW is more sensitivity to malignant tumors, and more stable to benign tumors, which is significant for further research of precision medicine.

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Automatic Classification of Fatty Liver Disease Based on Supervised Learning and Genetic Algorithm

Applied Sciences ◽

10.3390/app12010521 ◽

2022 ◽

Vol 12 (1) ◽

pp. 521

Author(s):

Ahmed Gaber ◽

Hassan A. Youness ◽

Alaa Hamdy ◽

Hammam M. Abdelaal ◽

Ammar M. Hassan

Keyword(s):

Liver Disease ◽

Fatty Liver ◽

Fatty Liver Disease ◽

Early Stage ◽

Ultrasound Image ◽

Image Features ◽

Machine Learning Algorithms ◽

Tissue Type ◽

Ultrasound Images

Fatty liver disease is considered a critical illness that should be diagnosed and detected at an early stage. In advanced stages, liver cancer or cirrhosis arise, and to identify this disease, radiologists commonly use ultrasound images. However, because of their low quality, radiologists found it challenging to recognize this disease using ultrasonic images. To avoid this problem, a Computer-Aided Diagnosis technique is developed in the current study, using Machine Learning Algorithms and a voting-based classifier to categorize liver tissues as being fatty or normal, based on extracting ultrasound image features and a voting-based classifier. Four main contributions are provided by our developed method: firstly, the classification of liver images is achieved as normal or fatty without a segmentation phase. Secondly, compared to our proposed work, the dataset in previous works was insufficient. A combination of 26 features is the third contribution. Based on the proposed methods, the extracted features are Gray-Level Co-Occurrence Matrix (GLCM) and First-Order Statistics (FOS). The fourth contribution is the voting classifier used to determine the liver tissue type. Several trials have been performed by examining the voting-based classifier and J48 algorithm on a dataset. The obtained TP, TN, FP, and FN were 94.28%, 97.14%, 5.71%, and 2.85%, respectively. The achieved precision, sensitivity, specificity, and F1-score were 94.28%, 97.05%, 94.44%, and 95.64%, respectively. The achieved classification accuracy using a voting-based classifier was 95.71% and in the case of using the J48 algorithm was 93.12%. The proposed work achieved a high performance compared with the research works.

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Breast Ultrasound Image Synthesis using Deep Convolutional Generative Adversarial Networks

Diagnostics ◽

10.3390/diagnostics9040176 ◽

2019 ◽

Vol 9 (4) ◽

pp. 176 ◽

Cited By ~ 8

Author(s):

Tomoyuki Fujioka ◽

Mio Mori ◽

Kazunori Kubota ◽

Yuka Kikuchi ◽

Leona Katsuta ◽

...

Keyword(s):

Ultrasound Image ◽

Image Synthesis ◽

Breast Ultrasound ◽

Generative Adversarial Networks ◽

Ultrasound Images ◽

Clinical Value ◽

Adversarial Networks ◽

Significant Difference ◽

Definition Of ◽

The Masses

Deep convolutional generative adversarial networks (DCGANs) are newly developed tools for generating synthesized images. To determine the clinical utility of synthesized images, we generated breast ultrasound images and assessed their quality and clinical value. After retrospectively collecting 528 images of 144 benign masses and 529 images of 216 malignant masses in the breasts, synthesized images were generated using a DCGAN with 50, 100, 200, 500, and 1000 epochs. The synthesized (n = 20) and original (n = 40) images were evaluated by two radiologists, who scored them for overall quality, definition of anatomic structures, and visualization of the masses on a five-point scale. They also scored the possibility of images being original. Although there was no significant difference between the images synthesized with 1000 and 500 epochs, the latter were evaluated as being of higher quality than all other images. Moreover, 2.5%, 0%, 12.5%, 37.5%, and 22.5% of the images synthesized with 50, 100, 200, 500, and 1000 epochs, respectively, and 14% of the original images were indistinguishable from one another. Interobserver agreement was very good (|r| = 0.708–0.825, p < 0.001). Therefore, DCGAN can generate high-quality and realistic synthesized breast ultrasound images that are indistinguishable from the original images.

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Surgical Management, Preoperative Tumor Localization, and Histopathology of 80 Patients Operated on for Insulinoma

The Journal of Clinical Endocrinology & Metabolism ◽

10.1210/jc.2019-01204 ◽

2019 ◽

Vol 104 (12) ◽

pp. 6129-6138 ◽

Cited By ~ 3

Author(s):

Mikkel Andreassen ◽

Emma Ilett ◽

Dominik Wiese ◽

Emily P Slater ◽

Marianne Klose ◽

...

Keyword(s):

Malignant Tumors ◽

Somatostatin Receptor ◽

Diagnostic Procedures ◽

Benign Tumors ◽

Receptor Subtype ◽

Receptor Imaging ◽

Venous Sampling ◽

Transabdominal Ultrasonography ◽

Positron Emission

Abstract Introduction Diagnosis and pathological classification of insulinomas are challenging. Aim To characterize localization of tumors, surgery outcomes, and histopathology in patients with insulinoma. Methods Patients with surgically resected sporadic insulinoma were included. Results Eighty patients were included. Seven had a malignant tumor. A total of 312 diagnostic examinations were performed: endoscopic ultrasonography (EUS; n = 59; sensitivity, 70%), MRI (n = 33; sensitivity, 58%), CT (n = 55; sensitivity, 47%), transabdominal ultrasonography (US; n = 45; sensitivity, 40%), somatostatin receptor imaging (n = 17; sensitivity, 29%), 18F-fluorodeoxyglucose positron emission tomography/CT (n = 1; negative), percutaneous transhepatic venous sampling (n = 10; sensitivity, 90%), arterial stimulation venous sampling (n = 20; sensitivity, 65%), and intraoperative US (n = 72; sensitivity, 89%). Fourteen tumors could not be visualized. Invasive methods were used in 7 of these 14 patients and localized the tumor in all cases. Median tumor size was 15 mm (range, 7 to 80 mm). Tumors with malignant vs benign behavior showed less staining for insulin (3 of 7 vs 66 of 73; P = 0.015) and for proinsulin (3 of 6 vs 58 of 59; P < 0.001). Staining for glucagon was seen in 2 of 6 malignant tumors and in no benign tumors (P < 0.001). Forty-three insulinomas stained negative for somatostatin receptor subtype 2a. Conclusion Localization of insulinomas requires many different diagnostic procedures. Most tumors can be localized by conventional imaging, including EUS. For nonvisible tumors, invasive methods may be a useful diagnostic tool. Malignant tumors showed reduced staining for insulin and proinsulin and increased staining for glucagon.

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Evaluation of Image Features Within and Surrounding Lesion Region for Risk Stratification in Breast Ultrasound Images

IETE Journal of Research ◽

10.1080/03772063.2019.1627918 ◽

2019 ◽

pp. 1-12

Author(s):

Lipismita Panigrahi ◽

Kesari Verma ◽

Bikesh Kumar Singh

Keyword(s):

Risk Stratification ◽

Breast Ultrasound ◽

Image Features ◽

Ultrasound Images

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Automated detection and classification of mass from breast ultrasound images

2015 Fifth National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG) ◽

10.1109/ncvpripg.2015.7490070 ◽

2015 ◽

Cited By ~ 4

Author(s):

Radhika V. Menon ◽

Poulami Raha ◽

Shweta Kothari ◽

Sumit Chakraborty ◽

Indrajit Chakrabarti ◽

...

Keyword(s):

Breast Ultrasound ◽

Automated Detection ◽

Ultrasound Images

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Adaptive Feature Selection for Ultrasound Image Processing and its Application to Liver Cirrhosis Detection

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.411-414.1372 ◽

2013 ◽

Vol 411-414 ◽

pp. 1372-1376

Author(s):

Wei Tin Lin ◽

Shyi Chyi Cheng ◽

Chih Lang Lin ◽

Chen Kuei Yang

Keyword(s):

Clustering Algorithm ◽

Ultrasound Image ◽

Image Features ◽

Ultrasound Images ◽

Analytic Hierarchy ◽

Low Level ◽

Adaptive Feature Selection ◽

High Level ◽

Objects Detection ◽

Hierarchy Process

An approach to improve the regions of interesting (ROIs) selection accuracy automatically for medical images is proposed. The aim of the study is to select the most interesting regions of image features that good for diffuse objects detection or classification. We use the AHP (Analytic Hierarchy Process) to obtain physicians high-level diagnosis vectors and are clustered using the well-known K-Means clustering algorithm. The system also automatically extracts low-level image features for improving to detect liver diseases from ultrasound images. The weights of low-level features are adaptively updated according the feature variances in the class. Finally, the high-level diagnosis decision is made based on the high-level diagnosis vectors for the top K near neighbors from the medical experts classified database. Experimental results show the effectiveness of the system.

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Clinical classification of cardiovascular tumors and tumor-like lesions, and its incidences

General Thoracic and Cardiovascular Surgery ◽

10.1007/s11748-013-0214-8 ◽

2013 ◽

Vol 61 (8) ◽

pp. 435-447 ◽

Cited By ~ 29

Author(s):

Jun Amano ◽

Jun Nakayama ◽

Yasuo Yoshimura ◽

Uichi Ikeda

Keyword(s):

Malignant Tumors ◽

Cardiac Myxoma ◽

World Health ◽

Benign Tumors ◽

Cardiac Tumors ◽

New Classification ◽

Metastatic Tumors ◽

Great Vessels ◽

Tumor Like Lesions

Abstract Tumors of the heart and the great vessels are very rare disease, and there are many disorders such as tumors originated from the heart and great vessels, metastatic tumors, and tumor-like lesions which do not fit into the usual concept of tumor or neoplasm; thus, it is very difficult to classify these tumors. We proposed a new classification of cardiovascular tumors for clinical use based on the accumulated biological analyses and clinical data of the reported literatures and our own study as benign tumors, malignant tumors, ectopic hyperplasia/ectopic tumors/others, and tumors of great vessels, with reference to the series of Atlas of tumor pathology of the Armed Forces Institute of Pathology and the recent World Health Organization classification of cardiac tumors issued in 2004. More than 50 disorders have been reported as tumors originated from the cardiovascular system, and various metastatic tumors from nearby organs, distant lesions, and intravascular extension tumors to the heart were reported. Based on the new classification, we reviewed epidemiology and incidence of cardiovascular tumors. Metastatic tumors are more frequent than tumors originated from the heart and great vessels, and cardiac myxoma is the most frequent tumors in all cardiac tumors.

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