[go: up one dir, main page]

Muhtadi et al., 2022 - Google Patents

Combined B-mode and Nakagami images for improved discrimination of breast masses using deep learning

Muhtadi et al., 2022

Document ID
11201717052325278671
Author
Muhtadi S
Haque S
Gallippi C
Publication year
Publication venue
2022 IEEE International Ultrasonics Symposium (IUS)

External Links

Snippet

Although ultrasound has become an important screening tool for the non-invasive diagnosis of breast cancer, it is limited by intra-and inter-observer variability, and subjectivity in diagnosis. On the other hand, deep learning-based approaches have the potential for …
Continue reading at ieeexplore.ieee.org (other versions)

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30004—Biomedical image processing
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B8/00—Diagnosis using ultrasonic, sonic or infrasonic waves
    • A61B8/52—Devices using data or image processing specially adapted for diagnosis using ultrasonic, sonic or infrasonic waves
    • A61B8/5215—Devices using data or image processing specially adapted for diagnosis using ultrasonic, sonic or infrasonic waves involving processing of medical diagnostic data
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B8/00—Diagnosis using ultrasonic, sonic or infrasonic waves
    • A61B8/08—Detecting organic movements or changes, e.g. tumours, cysts, swellings
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10132—Ultrasound image
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/0002—Inspection of images, e.g. flaw detection
    • G06T7/0012—Biomedical image inspection
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B8/00—Diagnosis using ultrasonic, sonic or infrasonic waves
    • A61B8/48—Diagnostic techniques
    • A61B8/488—Diagnostic techniques involving Doppler signals
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B8/00—Diagnosis using ultrasonic, sonic or infrasonic waves
    • A61B8/48—Diagnostic techniques
    • A61B8/485—Diagnostic techniques involving measuring strain or elastic properties
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10072—Tomographic images
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
    • G06F19/30—Medical informatics, i.e. computer-based analysis or dissemination of patient or disease data
    • G06F19/34—Computer-assisted medical diagnosis or treatment, e.g. computerised prescription or delivery of medication or diets, computerised local control of medical devices, medical expert systems or telemedicine
    • G06F19/345—Medical expert systems, neural networks or other automated diagnosis
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B8/00—Diagnosis using ultrasonic, sonic or infrasonic waves
    • A61B8/44—Constructional features of the ultrasonic, sonic or infrasonic diagnostic device
    • A61B8/4444—Constructional features of the ultrasonic, sonic or infrasonic diagnostic device related to the probe
    • A61B8/4472—Wireless probes
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B8/00—Diagnosis using ultrasonic, sonic or infrasonic waves
    • A61B8/13—Tomography
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details

Similar Documents

Publication Publication Date Title
Xu et al. Medical breast ultrasound image segmentation by machine learning
Joo et al. Computer-aided diagnosis of solid breast nodules: use of an artificial neural network based on multiple sonographic features
US8208697B2 (en) Method and apparatus for automatically developing a high performance classifier for producing medically meaningful descriptors in medical diagnosis imaging
US8142368B2 (en) Method of characterization and differentiation of tissue
Jarosik et al. Breast lesion classification based on ultrasonic radio-frequency signals using convolutional neural networks
Dicle Artificial intelligence in diagnostic ultrasonography
Naserameri et al. Enhancing Fatty Liver Disease Diagnosis Using Deep from Ultrasound Images
Muhtadi et al. Combined B-mode and Nakagami images for improved discrimination of breast masses using deep learning
Wu et al. Breast benign and malignant tumors rapidly screening by ARFI-VTI elastography and random decision forests based classifier
Neha et al. U-net in medical image segmentation: A review of its applications across modalities
Wu et al. Enhancement of ARFI-VTI elastography images in order to preliminary rapid screening of benign and malignant breast tumors using multilayer fractional-order machine vision classifier
Chen et al. Comparative analysis of logistic regression, support vector machine and artificial neural network for the differential diagnosis of benign and malignant solid breast tumors by the use of three-dimensional power Doppler imaging
Wiacek et al. Coherence metrics for reader-independent differentiation of cystic from solid breast masses in ultrasound images
Dai et al. B-mode ultrasound to elastography synthesis using multiscale learning
Uzunhisarcikli et al. A novel classifier model for mass classification using BI-RADS category in ultrasound images based on Type-2 fuzzy inference system
Nugroho et al. Active contour bilateral filter for breast lesions segmentation on ultrasound images
Sushma et al. Advancement in research techniques on medical imaging processing for breast cancer detection
Scheipers et al. Sonohistology for the computerized differentiation of parotid gland tumors
Muhtadi et al. Multimodal deep learning approaches to breast tumor characterization using ultrasound B-mode and Nakagami parametric images
Chang et al. Automatic detection of microcalcifications in breast ultrasound
CN117911775A (en) Dual-modality fusion can explain the automated intelligent diagnosis method of breast cancer
Sharma et al. Ultrasound-based classification of fatty liver disease: A review
Muhtadi et al. Analyzing the texture of Nakagami parametric images for classification of breast cancer
Amritha et al. Liver tumor segmentation and classification using deep learning
Marcomini et al. Quantitative evaluation of automatic methods for lesions detection in breast ultrasound images