AIMC Topic: Radiographic Image Enhancement

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A Hybridized ELM for Automatic Micro Calcification Detection in Mammogram Images Based on Multi-Scale Features.

Journal of medical systems
Detection of masses and micro calcifications are a stimulating task for radiologists in digital mammogram images. Radiologists using Computer Aided Detection (CAD) frameworks to find the breast lesion. Micro calcification may be the early sign of bre...

Convolutional Neural Networks for the Segmentation of Microcalcification in Mammography Imaging.

Journal of healthcare engineering
Cluster of microcalcifications can be an early sign of breast cancer. In this paper, we propose a novel approach based on convolutional neural networks for the detection and segmentation of microcalcification clusters. In this work, we used 283 mammo...

Contrast enhancement is a prognostic factor in IDH1/2 mutant, but not in wild-type WHO grade II/III glioma as confirmed by machine learning.

European journal of cancer (Oxford, England : 1990)
BACKGROUND: Mutation of the isocitrate dehydrogenase (IDH) gene and co-deletion on chromosome 1p/19q is becoming increasingly relevant for the evaluation of clinical outcome in glioma. Among the imaging parameters, contrast enhancement (CE) in WHO II...

Detection of Breast Cancer with Mammography: Effect of an Artificial Intelligence Support System.

Radiology
Purpose To compare breast cancer detection performance of radiologists reading mammographic examinations unaided versus supported by an artificial intelligence (AI) system. Materials and Methods An enriched retrospective, fully crossed, multireader, ...

SD-CNN: A shallow-deep CNN for improved breast cancer diagnosis.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Breast cancer is the second leading cause of cancer death among women worldwide. Nevertheless, it is also one of the most treatable malignances if detected early. Screening for breast cancer with full field digital mammography (FFDM) has been widely ...

Development and Validation of a Deep Learning System for Staging Liver Fibrosis by Using Contrast Agent-enhanced CT Images in the Liver.

Radiology
Purpose To develop and validate a deep learning system (DLS) for staging liver fibrosis by using CT images in the liver. Materials and Methods DLS for CT-based staging of liver fibrosis was created by using a development data set that included portal...

Small airway segmentation in thoracic computed tomography scans: a machine learning approach.

Physics in medicine and biology
Small airway obstruction is a main cause for chronic obstructive pulmonary disease (COPD). We propose a novel method based on machine learning to extract the airway system from a thoracic computed tomography (CT) scan. The emphasis of the proposed me...

A fully integrated computer-aided diagnosis system for digital X-ray mammograms via deep learning detection, segmentation, and classification.

International journal of medical informatics
A computer-aided diagnosis (CAD) system requires detection, segmentation, and classification in one framework to assist radiologists efficiently in an accurate diagnosis. In this paper, a completely integrated CAD system is proposed to screen digital...

Small lung nodules detection based on local variance analysis and probabilistic neural network.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: In medical examinations doctors use various techniques in order to provide to the patients an accurate analysis of their actual state of health. One of the commonly used methodologies is the x-ray screening. This examination...

A Novel Multiscale Gaussian-Matched Filter Using Neural Networks for the Segmentation of X-Ray Coronary Angiograms.

Journal of healthcare engineering
The accurate and efficient segmentation of coronary arteries in X-ray angiograms represents an essential task for computer-aided diagnosis. This paper presents a new multiscale Gaussian-matched filter (MGMF) based on artificial neural networks. The p...