AIMC Topic: Image Enhancement
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Method of Improved Fuzzy Contrast Combined Adaptive Threshold in NSCT for Medical Image Enhancement.
BioMed research international
Jun 28, 2017
Noises and artifacts are introduced to medical images due to acquisition techniques and systems. This interference leads to low contrast and distortion in images, which not only impacts the effectiveness of the medical image but also seriously affect...
Improving dense conditional random field for retinal vessel segmentation by discriminative feature learning and thin-vessel enhancement.
Computer methods and programs in biomedicine
Jun 24, 2017
BACKGROUND AND OBJECTIVES: As retinal vessels in color fundus images are thin and elongated structures, standard pairwise based random fields, which always suffer the "shrinking bias" problem, are not competent for such segmentation task. Recently, a...
Sparse Representation Based Multi-Instance Learning for Breast Ultrasound Image Classification.
Computational and mathematical methods in medicine
May 25, 2017
We propose a novel method based on sparse representation for breast ultrasound image classification under the framework of multi-instance learning (MIL). After image enhancement and segmentation, concentric circle is used to extract the global and lo...
Scanning double-sided documents without incurring show-through by learning to fuse two complementary images using multilayer perceptron.
PloS one
May 10, 2017
This paper presents a novel method for scanning duplex-printed documents without incurring the unwanted show-through artifact. The proposed method achieves the goal of eliminating the leaked-out reverse-side content by fusing a white backed scan imag...
Semi-automated brain tumor segmentation on multi-parametric MRI using regularized non-negative matrix factorization.
BMC medical imaging
May 4, 2017
BACKGROUND: Segmentation of gliomas in multi-parametric (MP-)MR images is challenging due to their heterogeneous nature in terms of size, appearance and location. Manual tumor segmentation is a time-consuming task and clinical practice would benefit ...
Differentiation of Enhancing Glioma and Primary Central Nervous System Lymphoma by Texture-Based Machine Learning.
AJNR. American journal of neuroradiology
Apr 27, 2017
BACKGROUND AND PURPOSE: Accurate preoperative differentiation of primary central nervous system lymphoma and enhancing glioma is essential to avoid unnecessary neurosurgical resection in patients with primary central nervous system lymphoma. The purp...
Computer vision-based diameter maps to study fluoroscopic recordings of small intestinal motility from conscious experimental animals.
Neurogastroenterology and motility
Mar 16, 2017
BACKGROUND: When available, fluoroscopic recordings are a relatively cheap, non-invasive and technically straightforward way to study gastrointestinal motility. Spatiotemporal maps have been used to characterize motility of intestinal preparations in...
Deep Learning in Medical Image Analysis.
Annual review of biomedical engineering
Mar 9, 2017
This review covers computer-assisted analysis of images in the field of medical imaging. Recent advances in machine learning, especially with regard to deep learning, are helping to identify, classify, and quantify patterns in medical images. At the ...
Performance of a Machine Learning Classifier of Knee MRI Reports in Two Large Academic Radiology Practices: A Tool to Estimate Diagnostic Yield.
AJR. American journal of roentgenology
Jan 31, 2017
OBJECTIVE: The purpose of this study is to evaluate the performance of a natural language processing (NLP) system in classifying a database of free-text knee MRI reports at two separate academic radiology practices.
Humans
North Carolina
Radiology Department, Hospital
Knee
Support Vector Machine
Image Enhancement
Machine Learning
Data Mining
Magnetic Resonance Imaging
Natural Language Processing
Academic Medical Centers
Reproducibility of Results
California
Workload
Pattern Recognition, Automated
Radiology Information Systems
Sensitivity and Specificity
Implementing Machine Learning in Radiology Practice and Research.
AJR. American journal of roentgenology
Jan 26, 2017
OBJECTIVE: The purposes of this article are to describe concepts that radiologists should understand to evaluate machine learning projects, including common algorithms, supervised as opposed to unsupervised techniques, statistical pitfalls, and data ...
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