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Diagnostics
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The Advanced Role of Medical Image Segmentation in Computer-Aided Diagnosis
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The Advanced Role of Medical Image Segmentation in Computer-Aided Diagnosis

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Machine Learning and Artificial Intelligence in Diagnostics".

Deadline for manuscript submissions: closed (30 September 2023) | Viewed by 3117

Special Issue Editor

Prof. Dr. Khalid M. Hosny

E-Mail Website
Guest Editor
Department of Information Technology, Zagazig University, Zagazig 44519, Egypt
Interests: medical image analysis; segmentation; skin lesion; diagnosis; CAD systems

Special Issue Information

Dear Colleagues,

Medical image segmentation is an essential component of computer-aided diagnosis systems (CADs), in which accurate segmentation is vital to achieving perfect disease diagnoses. Medical images from different imaging modalities play a crucial role in the early detection of cancer, tumors, and diagnosis of diseases. Various devices are used to image all parts of the human body, which leads to variability in the objects detected in medical images. In addition, the medical imaging of interior and exterior parts of the human body results in images with varying contrasts. Furthermore, in general, medical images are associated with noise. Due to these factors, the segmentation of medical images can be challenging.

Deep learning-based segmentation methods and hybrid methods can be used to segment grayscale and color medical images. This Special Issue addresses all aspects of medical image segmentation and its advanced role in computer-aided diagnosis. This Special Issue particularly focuses on the optimization-based segmentation methods of 2D and volumetric medical images. We invite researchers to contribute to this Special Issue with original research articles and review articles that concentrate on the abovementioned areas of interest.

Prof. Dr. Khalid M. Hosny
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. Diagnostics is an international peer-reviewed open access semimonthly 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 2600 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

  • medical images
  • segmentation
  • classification
  • computer-aided diagnosis systems
  • optimization algorithms
  • deep learning

Published Papers (2 papers)

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Research

23 pages, 10288 KiB  
Article
Hyper-Dense_Lung_Seg: Multimodal-Fusion-Based Modified U-Net for Lung Tumour Segmentation Using Multimodality of CT-PET Scans
by Goram Mufarah Alshmrani, Qiang Ni, Richard Jiang and Nada Muhammed
Diagnostics 2023, 13(22), 3481; https://doi.org/10.3390/diagnostics13223481 - 20 Nov 2023
Cited by 1 | Viewed by 1015
Abstract
The majority of cancer-related deaths globally are due to lung cancer, which also has the second-highest mortality rate. The segmentation of lung tumours, treatment evaluation, and tumour stage classification have become significantly more accessible with the advent of PET/CT scans. With the advent [...] Read more.
The majority of cancer-related deaths globally are due to lung cancer, which also has the second-highest mortality rate. The segmentation of lung tumours, treatment evaluation, and tumour stage classification have become significantly more accessible with the advent of PET/CT scans. With the advent of PET/CT scans, it is possible to obtain both functioning and anatomic data during a single examination. However, integrating images from different modalities can indeed be time-consuming for medical professionals and remains a challenging task. This challenge arises from several factors, including differences in image acquisition techniques, image resolutions, and the inherent variations in the spectral and temporal data captured by different imaging modalities. Artificial Intelligence (AI) methodologies have shown potential in the automation of image integration and segmentation. To address these challenges, multimodal fusion approach-based U-Net architecture (early fusion, late fusion, dense fusion, hyper-dense fusion, and hyper-dense VGG16 U-Net) are proposed for lung tumour segmentation. Dice scores of 73% show that hyper-dense VGG16 U-Net is superior to the other four proposed models. The proposed method can potentially aid medical professionals in detecting lung cancer at an early stage. Full article
(This article belongs to the Special Issue The Advanced Role of Medical Image Segmentation in Computer-Aided Diagnosis)
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30 pages, 58440 KiB  
Article
Multilevel Threshold Segmentation of Skin Lesions in Color Images Using Coronavirus Optimization Algorithm
by Yousef S. Alsahafi, Doaa S. Elshora, Ehab R. Mohamed and Khalid M. Hosny
Diagnostics 2023, 13(18), 2958; https://doi.org/10.3390/diagnostics13182958 - 15 Sep 2023
Cited by 1 | Viewed by 898
Abstract
Skin Cancer (SC) is among the most hazardous due to its high mortality rate. Therefore, early detection of this disease would be very helpful in the treatment process. Multilevel Thresholding (MLT) is widely used for extracting regions of interest from medical images. Therefore, [...] Read more.
Skin Cancer (SC) is among the most hazardous due to its high mortality rate. Therefore, early detection of this disease would be very helpful in the treatment process. Multilevel Thresholding (MLT) is widely used for extracting regions of interest from medical images. Therefore, this paper utilizes the recent Coronavirus Disease Optimization Algorithm (COVIDOA) to address the MLT issue of SC images utilizing the hybridization of Otsu, Kapur, and Tsallis as fitness functions. Various SC images are utilized to validate the performance of the proposed algorithm. The proposed algorithm is compared to the following five meta-heuristic algorithms: Arithmetic Optimization Algorithm (AOA), Sine Cosine Algorithm (SCA), Reptile Search Algorithm (RSA), Flower Pollination Algorithm (FPA), Seagull Optimization Algorithm (SOA), and Artificial Gorilla Troops Optimizer (GTO) to prove its superiority. The performance of all algorithms is evaluated using a variety of measures, such as Mean Square Error (MSE), Peak Signal-To-Noise Ratio (PSNR), Feature Similarity Index Metric (FSIM), and Normalized Correlation Coefficient (NCC). The results of the experiments prove that the proposed algorithm surpasses several competing algorithms in terms of MSE, PSNR, FSIM, and NCC segmentation metrics and successfully solves the segmentation issue. Full article
(This article belongs to the Special Issue The Advanced Role of Medical Image Segmentation in Computer-Aided Diagnosis)
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