Topological Feature Analysis of RGB Image Based on Persistent Homology

2021 ◽  
pp. 415-426
Author(s):  
Jian Ma ◽  
Lizhi Zhang ◽  
Huadong Sun ◽  
Zhijie Zhao
Keyword(s):  
Persistent Homology ◽  
Feature Analysis ◽  
Topological Feature ◽  
Rgb Image

scholarly journals Persistent Homology of Tumor CT Scans Predicts Survival In Lung Cancer

2020 ◽  
Author(s):  
Eashwar Somasundaram ◽  
Adam Litzler ◽  
Raoul R. Wadhwa ◽  
Jacob G. Scott
Keyword(s):  
Lung Cancer ◽  
Clinical Oncology ◽  
Lung Tumor ◽  
Persistent Homology ◽  
Cox Proportional Hazards Model ◽  
Ct Scans ◽  
Topological Feature ◽  
Image Size ◽  
Topological Features ◽  
First Moment

ABSTRACTRadiomics, the objective study of non-visual features in clinical imaging, has been useful in informing decisions in clinical oncology. However, radiomics currently lacks the ability to characterize the overall structure of the data. This field may benefit by incorporating persistent homology, a popular new algorithm that analyzes whole data structure. We hypothesized that persistent homology could be applied to lung tumor scans and predict clinical variables. We obtained computed tomography lung scans (n = 565) from the NSCLC-Radiomics and NSCLC-Radiogenomics datasets in The Cancer Imaging Archive. Segmentation data was used to create a cubical region centered on the primary tumor in each scan. For each scan, a cubical complex filtration based on Hounsfield units was generated. We created a feature curve that plotted the number of 0 dimensional topological features against each Hounsfield unit. The curve’s first moment of the distribution was utilized as a summary statistic to predict survival in a Cox proportional hazards model. The first moment of the distribution is equivalent to the area under the curve of our topological feature curves (AUC). The Kruskal-Wallis H Test and a post-hoc Dunn’s test with Bonferroni correction were used to test AUC differences among survival quartiles. After controlling for tumor image size, age, and stage, AUC, was associated with poorer survival (HR = 1.118; 95% CI = 1.026-1.218; p = 0.01). AUC was significantly higher for patients in the lowest survival quartile compared to the highest survival quartile (p < 0.001). We have shown that persistent homology can generate useful clinical correlates from tumor CT scans. Our 0-dimensional topological feature curve statistic predicts survival in lung cancer patients. This novel statistic may be used in tandem with standard radiomics variables to better inform clinical oncology decisions.

Phonomotor Versus Semantic Feature Analysis Treatment for Anomia in 58 Persons With Aphasia: A Randomized Controlled Trial

2019 ◽  
Vol 62 (12) ◽  
pp. 4464-4482 ◽  
Author(s):  
Diane L. Kendall ◽  
Megan Oelke Moldestad ◽  
Wesley Allen ◽  
Janaki Torrence ◽  
Stephen E. Nadeau
Keyword(s):  
Randomized Controlled Trial ◽  
Response Latency ◽  
Controlled Trial ◽  
Semantic Knowledge ◽  
Semantic Feature ◽  
Feature Analysis ◽  
Group Differences ◽  
Randomized Controlled ◽  
Confrontation Naming ◽  
Semantic Feature Analysis

Purpose The ultimate goal of anomia treatment should be to achieve gains in exemplars trained in the therapy session, as well as generalization to untrained exemplars and contexts. The purpose of this study was to test the efficacy of phonomotor treatment, a treatment focusing on enhancement of phonological sequence knowledge, against semantic feature analysis (SFA), a lexical-semantic therapy that focuses on enhancement of semantic knowledge and is well known and commonly used to treat anomia in aphasia. Method In a between-groups randomized controlled trial, 58 persons with aphasia characterized by anomia and phonological dysfunction were randomized to receive 56–60 hr of intensively delivered treatment over 6 weeks with testing pretreatment, posttreatment, and 3 months posttreatment termination. Results There was no significant between-groups difference on the primary outcome measure (untrained nouns phonologically and semantically unrelated to each treatment) at 3 months posttreatment. Significant within-group immediately posttreatment acquisition effects for confrontation naming and response latency were observed for both groups. Treatment-specific generalization effects for confrontation naming were observed for both groups immediately and 3 months posttreatment; a significant decrease in response latency was observed at both time points for the SFA group only. Finally, significant within-group differences on the Comprehensive Aphasia Test–Disability Questionnaire ( Swinburn, Porter, & Howard, 2004 ) were observed both immediately and 3 months posttreatment for the SFA group, and significant within-group differences on the Functional Outcome Questionnaire ( Glueckauf et al., 2003 ) were found for both treatment groups 3 months posttreatment. Discussion Our results are consistent with those of prior studies that have shown that SFA treatment and phonomotor treatment generalize to untrained words that share features (semantic or phonological sequence, respectively) with the training set. However, they show that there is no significant generalization to untrained words that do not share semantic features or phonological sequence features.

Robust Multispectral Data Hiding in RGB Image Using Digital Watermarking

2016 ◽  
Vol 2016 (1) ◽  
pp. 213-218
Author(s):  
Kazushige Banzawa ◽  
Kazuma Shinoda ◽  
Madoka Hasegawa
Keyword(s):  
Digital Watermarking ◽  
Data Hiding ◽  
Multispectral Data ◽  
Rgb Image

Developing an Inkjet Printer I: RGB Image to CMY Ink Amounts - Image Processing and Color Management

2020 ◽  
Vol 2020 (15) ◽  
pp. 350-1-350-10
Author(s):  
Yin Wang ◽  
Baekdu Choi ◽  
Davi He ◽  
Zillion Lin ◽  
George Chiu ◽  
...  
Keyword(s):  
Image Processing ◽  
Low Cost ◽  
Experimental Results ◽  
Color Management ◽  
Finger Nail ◽  
Inkjet Printer ◽  
Rgb Image ◽  
Novel Algorithm

In this paper, we will introduce a novel low-cost, small size, portable nail printer. The usage of this system is to print any desired pattern on a finger nail in just a few minutes. The detailed pre-processing procedures will be described in this paper. These include image processing to find the correct printing zone, and color management to match the patterns’ color. In each phase, a novel algorithm will be introduced to refine the result. The paper will state the mathematical principles behind each phase, and show the experimental results, which illustrate the algorithms’ capabilities to handle the task.

Variable Precision Depth Encoding for 3D Range Geometry Compression

2020 ◽  
Vol 2020 (17) ◽  
pp. 34-1-34-7
Author(s):  
Matthew G. Finley ◽  
Tyler Bell
Keyword(s):  
Normal Distribution ◽  
Arbitrary Distribution ◽  
File Size ◽  
Geometry Compression ◽  
Variable Precision ◽  
Wide Range ◽  
Novel Method ◽  
Encoding Method ◽  
Rgb Image ◽  
Color Channels

This paper presents a novel method for accurately encoding 3D range geometry within the color channels of a 2D RGB image that allows the encoding frequency—and therefore the encoding precision—to be uniquely determined for each coordinate. The proposed method can thus be used to balance between encoding precision and file size by encoding geometry along a normal distribution; encoding more precisely where the density of data is high and less precisely where the density is low. Alternative distributions may be followed to produce encodings optimized for specific applications. In general, the nature of the proposed encoding method is such that the precision of each point can be freely controlled or derived from an arbitrary distribution, ideally enabling this method for use within a wide range of applications.

Feature analysis of functional MRI for discrimination between normal and epileptogenic brain

2007 ◽  
Author(s):  
Lauren S. Burrell ◽  
Simon M. Glynn ◽  
George J. Vachtsevanos ◽  
Brian Litt
Keyword(s):  
Functional Mri ◽  
Feature Analysis

Analysis of color Image Enhancement Using DWT, Wavelet Shrinkageand FHE Methods

2017 ◽  
Vol 7 (8) ◽  
pp. 56
Author(s):  
Ashish Dwivedi ◽  
Nirupma Tiwari
Keyword(s):  
Image Enhancement ◽  
Color Image ◽  
Human Perception ◽  
Histogram Equalization ◽  
Wavelet Shrinkage ◽  
Visual Appearance ◽  
Enhancement Method ◽  
Fuzzy Histogram ◽  
Rgb Image

Image enhancement (IE) is very important in the field where visual appearance of an image is the main. Image enhancement is the process of improving the image in such a way that the resulting or output image is more suitable than the original image for specific task. With the help of image enhancement process the quality of image can be improved to get good quality images so that they can be clear for human perception or for the further analysis done by machines.Image enhancement method enhances the quality, visual appearance, improves clarity of images, removes blurring and noise, increases contrast and reveals details. The aim of this paper is to study and determine limitations of the existing IE techniques. This paper will provide an overview of different IE techniques commonly used. We Applied DWT on original RGB image then we applied FHE (Fuzzy Histogram Equalization) after DWT we have done the wavelet shrinkage on Three bands (LH, HL, HH). After that we fuse the shrinkage image and FHE image together and we get the enhance image.

Sign in / Sign up

Export Citation Format

Share Document