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J. Imaging
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Advances in Image Analysis: Shapes, Textures and Multifractals
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Advances in Image Analysis: Shapes, Textures and Multifractals

A special issue of Journal of Imaging (ISSN 2313-433X).

Deadline for manuscript submissions: closed (31 July 2024) | Viewed by 16982

Special Issue Editor

Prof. Dr. Ramakrishnan Mukundan

E-Mail Website
Guest Editor
Department of Computer Science and Software Engineering, University of Canterbury, Christchurch 8041, New Zealand
Interests: biomedical image analysis; machine learning; texture descriptors; real-time rendering and animation

Special Issue Information

Dear Colleagues,

Shape and texture play very important roles in characterising image features in the fields of computer vision, image analysis and machine learning. Various types of shape descriptors, such as image moments, area, circularity, convexity and major axis orientation, are commonly used for quantifying shape information. Popular texture features used in applications involving segmentation and classification are Grey Level Dependence/Co-Occurrence/Run-Length Matrices and Local Binary/Ternary/Quinary Patterns.

Multifractal features are now increasingly being used in biomedical imaging and machine learning applications. Traditional generalizations of fractal systems based on a range of singularity exponents and multifractal spectra have now found their way into the domain of image analysis as techniques for representing intensity (or color) variations around pixel neighborhoods. The singularity spectrum of intensity variations in an image has been shown to contain highly useful information related to both the shape and texture characteristics needed for the effective identification and classification of regions of interest. Several new multifractal analysis methods have been recently reported in the field of medical image processing.  These include algorithms for microcalcification detection in mammograms, analysis of tissue structures in histopathological images, nuclei segmentation in whole slide images, emphysema classification in CT images, feature enhancement in ultrasound videos and mammographic breast density estimation.

This Special Issue aims to promote further research into all aspects of shape/texture analysis and applications of multifractal measures and descriptors. We welcome original contributions showing the effectiveness of shape, texture and multifractal features in image segmentation, feature analysis, shape analysis, image classification and biomarker discovery.

Prof. Dr. Ramakrishnan Mukundan
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Imaging is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • image texture descriptors
  • texture feature extraction
  • multifractal analysis
  • singularity spectrum
  • tissue image analysis
  • image classification
  • region of interest segmentation

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Published Papers (5 papers)

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Research

11 pages, 2484 KiB  
Article
Mitigating Interobserver Variability in Radiomics with ComBat: A Feasibility Study
by Alessia D’Anna, Giuseppe Stella, Anna Maria Gueli, Carmelo Marino and Alfredo Pulvirenti
J. Imaging 2024, 10(11), 270; https://doi.org/10.3390/jimaging10110270 - 24 Oct 2024
Viewed by 1105
Abstract
This study investigates Intraobserver Features Variability (IFV) in radiomics studies and assesses the effectiveness of the ComBat harmonization method in mitigating these effects. Methods: This study utilizes data from the NSCLC-Radiomics-Interobserver1 dataset, comprising CT scans of 22 Non-Small Cell Lung Cancer (NSCLC) patients, [...] Read more.
This study investigates Intraobserver Features Variability (IFV) in radiomics studies and assesses the effectiveness of the ComBat harmonization method in mitigating these effects. Methods: This study utilizes data from the NSCLC-Radiomics-Interobserver1 dataset, comprising CT scans of 22 Non-Small Cell Lung Cancer (NSCLC) patients, with multiple Gross Tumor Volume (GTV) delineations performed by five radiation oncologists. Segmentation was completed manually (“vis”) or by autosegmentation with manual editing (“auto”). A total of 1229 radiomic features were extracted from each GTV, segmentation method, and oncologist. Features extracted included first order, shape, GLCM, GLRLM, GLSZM, and GLDM from original, wavelet-filtered, and LoG-filtered images. Results: Before implementing ComBat harmonization, 83% of features exhibited p-values below 0.05 in the “vis” approach; this percentage decreased to 34% post-harmonization. Similarly, for the “auto” approach, 75% of features demonstrated statistical significance prior to ComBat, but this figure declined to 33% after its application. Among a subset of three expert radiation oncologists, percentages changed from 77% to 25% for “vis” contouring and from 64% to 23% for “auto” contouring. This study demonstrates that ComBat harmonization could effectively reduce IFV, enhancing the feasibility of multicenter radiomics studies. It also highlights the significant impact of physician experience on radiomics analysis outcomes. Full article
(This article belongs to the Special Issue Advances in Image Analysis: Shapes, Textures and Multifractals)
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13 pages, 2464 KiB  
Article
Radiomics Texture Analysis of Bone Marrow Alterations in MRI Knee Examinations
by Spiros Kostopoulos, Nada Boci, Dionisis Cavouras, Antonios Tsagkalis, Maria Papaioannou, Alexandra Tsikrika, Dimitris Glotsos, Pantelis Asvestas and Eleftherios Lavdas
J. Imaging 2023, 9(11), 252; https://doi.org/10.3390/jimaging9110252 - 20 Nov 2023
Cited by 5 | Viewed by 2328
Abstract
Accurate diagnosis and timely intervention are key to addressing common knee conditions effectively. In this work, we aim to identify textural changes in knee lesions based on bone marrow edema (BME), injury (INJ), and osteoarthritis (OST). One hundred and twenty-one MRI knee examinations [...] Read more.
Accurate diagnosis and timely intervention are key to addressing common knee conditions effectively. In this work, we aim to identify textural changes in knee lesions based on bone marrow edema (BME), injury (INJ), and osteoarthritis (OST). One hundred and twenty-one MRI knee examinations were selected. Cases were divided into three groups based on radiological findings: forty-one in the BME, thirty-seven in the INJ, and forty-three in the OST groups. From each ROI, eighty-one radiomic descriptors were calculated, encoding texture information. The results suggested differences in the texture characteristics of regions of interest (ROIs) extracted from PD-FSE and STIR sequences. We observed that the ROIs associated with BME exhibited greater local contrast and a wider range of structural diversity compared to the ROIs corresponding to OST. When it comes to STIR sequences, the ROIs related to BME showed higher uniformity in terms of both signal intensity and the variability of local structures compared to the INJ ROIs. A combined radiomic descriptor managed to achieve a high separation ability, with AUC of 0.93 ± 0.02 in the test set. Radiomics analysis may provide a non-invasive and quantitative means to assess the spatial distribution and heterogeneity of bone marrow edema, aiding in its early detection and characterization. Full article
(This article belongs to the Special Issue Advances in Image Analysis: Shapes, Textures and Multifractals)
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14 pages, 1760 KiB  
Article
Assessing Acetabular Index Angle in Infants: A Deep Learning-Based Novel Approach
by Farmanullah Jan, Atta Rahman, Roaa Busaleh, Haya Alwarthan, Samar Aljaser, Sukainah Al-Towailib, Safiyah Alshammari, Khadeejah Rasheed Alhindi, Asrar Almogbil, Dalal A. Bubshait and Mohammed Imran Basheer Ahmed
J. Imaging 2023, 9(11), 242; https://doi.org/10.3390/jimaging9110242 - 6 Nov 2023
Cited by 9 | Viewed by 4719
Abstract
Developmental dysplasia of the hip (DDH) is a disorder characterized by abnormal hip development that frequently manifests in infancy and early childhood. Preventing DDH from occurring relies on a timely and accurate diagnosis, which requires careful assessment by medical specialists during early X-ray [...] Read more.
Developmental dysplasia of the hip (DDH) is a disorder characterized by abnormal hip development that frequently manifests in infancy and early childhood. Preventing DDH from occurring relies on a timely and accurate diagnosis, which requires careful assessment by medical specialists during early X-ray scans. However, this process can be challenging for medical personnel to achieve without proper training. To address this challenge, we propose a computational framework to detect DDH in pelvic X-ray imaging of infants that utilizes a pipelined deep learning-based technique consisting of two stages: instance segmentation and keypoint detection models to measure acetabular index angle and assess DDH affliction in the presented case. The main aim of this process is to provide an objective and unified approach to DDH diagnosis. The model achieved an average pixel error of 2.862 ± 2.392 and an error range of 2.402 ± 1.963° for the acetabular angle measurement relative to the ground truth annotation. Ultimately, the deep-learning model will be integrated into the fully developed mobile application to make it easily accessible for medical specialists to test and evaluate. This will reduce the burden on medical specialists while providing an accurate and explainable DDH diagnosis for infants, thereby increasing their chances of successful treatment and recovery. Full article
(This article belongs to the Special Issue Advances in Image Analysis: Shapes, Textures and Multifractals)
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15 pages, 5524 KiB  
Article
Content-Based Image Retrieval for Traditional Indonesian Woven Fabric Images Using a Modified Convolutional Neural Network Method
by Silvester Tena, Rudy Hartanto and Igi Ardiyanto
J. Imaging 2023, 9(8), 165; https://doi.org/10.3390/jimaging9080165 - 18 Aug 2023
Cited by 7 | Viewed by 3414
Abstract
A content-based image retrieval system, as an Indonesian traditional woven fabric knowledge base, can be useful for artisans and trade promotions. However, creating an effective and efficient retrieval system is difficult due to the lack of an Indonesian traditional woven fabric dataset, and [...] Read more.
A content-based image retrieval system, as an Indonesian traditional woven fabric knowledge base, can be useful for artisans and trade promotions. However, creating an effective and efficient retrieval system is difficult due to the lack of an Indonesian traditional woven fabric dataset, and unique characteristics are not considered simultaneously. One type of traditional Indonesian fabric is ikat woven fabric. Thus, this study collected images of this traditional Indonesian woven fabric to create the TenunIkatNet dataset. The dataset consists of 120 classes and 4800 images. The images were captured perpendicularly, and the ikat woven fabrics were placed on different backgrounds, hung, and worn on the body, according to the utilization patterns. The feature extraction method using a modified convolutional neural network (MCNN) learns the unique features of Indonesian traditional woven fabrics. The experimental results show that the modified CNN model outperforms other pretrained CNN models (i.e., ResNet101, VGG16, DenseNet201, InceptionV3, MobileNetV2, Xception, and InceptionResNetV2) in top-5, top-10, top-20, and top-50 accuracies with scores of 99.96%, 99.88%, 99.50%, and 97.60%, respectively. Full article
(This article belongs to the Special Issue Advances in Image Analysis: Shapes, Textures and Multifractals)
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21 pages, 6021 KiB  
Article
Novel Texture Feature Descriptors Based on Multi-Fractal Analysis and LBP for Classifying Breast Density in Mammograms
by Haipeng Li, Ramakrishnan Mukundan and Shelley Boyd
J. Imaging 2021, 7(10), 205; https://doi.org/10.3390/jimaging7100205 - 6 Oct 2021
Cited by 8 | Viewed by 4218
Abstract
This paper investigates the usefulness of multi-fractal analysis and local binary patterns (LBP) as texture descriptors for classifying mammogram images into different breast density categories. Multi-fractal analysis is also used in the pre-processing step to segment the region of interest (ROI). We use [...] Read more.
This paper investigates the usefulness of multi-fractal analysis and local binary patterns (LBP) as texture descriptors for classifying mammogram images into different breast density categories. Multi-fractal analysis is also used in the pre-processing step to segment the region of interest (ROI). We use four multi-fractal measures and the LBP method to extract texture features, and to compare their classification performance in experiments. In addition, a feature descriptor combining multi-fractal features and multi-resolution LBP (MLBP) features is proposed and evaluated in this study to improve classification accuracy. An autoencoder network and principal component analysis (PCA) are used for reducing feature redundancy in the classification model. A full field digital mammogram (FFDM) dataset, INBreast, which contains 409 mammogram images, is used in our experiment. BI-RADS density labels given by radiologists are used as the ground truth to evaluate the classification results using the proposed methods. Experimental results show that the proposed feature descriptor based on multi-fractal features and LBP result in higher classification accuracy than using individual texture feature sets. Full article
(This article belongs to the Special Issue Advances in Image Analysis: Shapes, Textures and Multifractals)
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