Special Issue "Fuzzy Logic for Image Processing"

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: closed (30 June 2017)

Special Issue Editors

Guest Editor
Prof. Dr. Giovanna Castellano

Department of Informatics, University of Bari "Aldo Moro", Bari, Italy
Website | E-Mail
Phone: +39 80 5442456
Interests: image processing; fuzzy logic; pattern recognition; computer vision; fuzzy clustering
Guest Editor
Prof. Dr. Laura Caponetti

Department of Informatics, University of Bari "Aldo Moro", Bari, Italy
Website | E-Mail
Interests: image processing; fuzzy logic; pattern recognition; computer vision; fuzzy clustering

Special Issue Information

Dear Colleagues,

The increasing availability of huge image collections in different application fields, such as medical diagnosis, remote sensing, transmission and encoding, machine/robot vision, video processing, microscopic imaging has pressed the need, in the last few last years, for the development of efficient techniques capable of managing/processing large collection of image data, and which are suitable for analysis, indexing and retrieval of image data.

Classical image processing methods often face great difficulties while dealing with images containing noise and distortions. Under such conditions, fuzzy logic techniques turn out to be effective to address challenging real-world image processing problems. The main objective of this Special Issue is to show the potential of fuzzy techniques in challenging applications involving tasks related to understand, represent, and process digital images.

This Special Issue will focus on the exploration of the fundamental roles, as well as practical impacts of fuzzy techniques, in the field of image processing. Prospective authors are invited to submit previously unpublished works in these areas. Possible topics include (but are not limited to) applications of fuzzy logic in image processing, leading to advanced fuzzy techniques for:

  • Low-medium level image processing: Contrast enhancement, edge detection, noise detection and removal, segmentation, geometric measurement, and clustering of image data
  • High-level image/scene analysis: Pattern recognition and scene description, modeling of image data, object matching, image annotation, and image retrieval
  • Real-world applications, such as medical/biological image segmentation, face recognition, industrial product inspection, automated surveillance, finger print and biometric security systems.

Time Schedule & Deadlines
Abstract Submission: 15 March 2017
Acceptance Notification: 30 March 2017
Manuscript Submissions: 30 June 2017

Prof. Dr. Giovanna Castellano
Prof. Dr. Laura Caponetti
Guest Editors

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 papers will be 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. Information 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 850 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.

Published Papers (7 papers)

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Editorial

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Open AccessEditorial Special Issue on Fuzzy Logic for Image Processing
Information 2018, 9(1), 3; doi:10.3390/info9010003
Received: 24 December 2017 / Revised: 24 December 2017 / Accepted: 24 December 2017 / Published: 27 December 2017
PDF Full-text (152 KB) | HTML Full-text | XML Full-text
Abstract
The increasing availability of huge image collections in different application fields, such as medical diagnosis, remote sensing, transmission and encoding, machine/robot vision, and video processing, microscopic imaging has pressed the need, in the last few last years, for the development of efficient techniques
[...] Read more.
The increasing availability of huge image collections in different application fields, such as medical diagnosis, remote sensing, transmission and encoding, machine/robot vision, and video processing, microscopic imaging has pressed the need, in the last few last years, for the development of efficient techniques capable of managing and processing large collection of image data [...] Full article
(This article belongs to the Special Issue Fuzzy Logic for Image Processing)

Research

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Open AccessArticle Land Cover Classification from Multispectral Data Using Computational Intelligence Tools: A Comparative Study
Information 2017, 8(4), 147; doi:10.3390/info8040147
Received: 8 October 2017 / Revised: 7 November 2017 / Accepted: 13 November 2017 / Published: 15 November 2017
Cited by 1 | PDF Full-text (7230 KB) | HTML Full-text | XML Full-text
Abstract
This article discusses how computational intelligence techniques are applied to fuse spectral images into a higher level image of land cover distribution for remote sensing, specifically for satellite image classification. We compare a fuzzy-inference method with two other computational intelligence methods, decision trees
[...] Read more.
This article discusses how computational intelligence techniques are applied to fuse spectral images into a higher level image of land cover distribution for remote sensing, specifically for satellite image classification. We compare a fuzzy-inference method with two other computational intelligence methods, decision trees and neural networks, using a case study of land cover classification from satellite images. Further, an unsupervised approach based on k-means clustering has been also taken into consideration for comparison. The fuzzy-inference method includes training the classifier with a fuzzy-fusion technique and then performing land cover classification using reinforcement aggregation operators. To assess the robustness of the four methods, a comparative study including three years of land cover maps for the district of Mandimba, Niassa province, Mozambique, was undertaken. Our results show that the fuzzy-fusion method performs similarly to decision trees, achieving reliable classifications; neural networks suffer from overfitting; while k-means clustering constitutes a promising technique to identify land cover types from unknown areas. Full article
(This article belongs to the Special Issue Fuzzy Logic for Image Processing)
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Open AccessFeature PaperArticle MR Brain Image Segmentation: A Framework to Compare Different Clustering Techniques
Information 2017, 8(4), 138; doi:10.3390/info8040138
Received: 8 October 2017 / Revised: 1 November 2017 / Accepted: 1 November 2017 / Published: 4 November 2017
Cited by 2 | PDF Full-text (1124 KB) | HTML Full-text | XML Full-text
Abstract
In Magnetic Resonance (MR) brain image analysis, segmentation is commonly used for detecting, measuring and analyzing the main anatomical structures of the brain and eventually identifying pathological regions. Brain image segmentation is of fundamental importance since it helps clinicians and researchers to concentrate
[...] Read more.
In Magnetic Resonance (MR) brain image analysis, segmentation is commonly used for detecting, measuring and analyzing the main anatomical structures of the brain and eventually identifying pathological regions. Brain image segmentation is of fundamental importance since it helps clinicians and researchers to concentrate on specific regions of the brain in order to analyze them. However, segmentation of brain images is a difficult task due to high similarities and correlations of intensity among different regions of the brain image. Among various methods proposed in the literature, clustering algorithms prove to be successful tools for image segmentation. In this paper, we present a framework for image segmentation that is devoted to support the expert in identifying different brain regions for further analysis. The framework includes different clustering methods to perform segmentation of MR images. Furthermore, it enables easy comparison of different segmentation results by providing a quantitative evaluation using an entropy-based measure as well as other measures commonly used to evaluate segmentation results. To show the potential of the framework, the implemented clustering methods are compared on simulated T1-weighted MR brain images from the Internet Brain Segmentation Repository (IBSR database) provided with ground truth segmentation. Full article
(This article belongs to the Special Issue Fuzzy Logic for Image Processing)
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Open AccessArticle Edge Detection Method Based on General Type-2 Fuzzy Logic Applied to Color Images
Information 2017, 8(3), 104; doi:10.3390/info8030104
Received: 1 July 2017 / Revised: 22 August 2017 / Accepted: 25 August 2017 / Published: 28 August 2017
Cited by 1 | PDF Full-text (24536 KB) | HTML Full-text | XML Full-text
Abstract
This paper presents a new general type-2 fuzzy logic method for edge detection applied to color format images. The proposed algorithm combines the methodology based on the image gradients and general type-2 fuzzy logic theory to provide a powerful edge detection method. General
[...] Read more.
This paper presents a new general type-2 fuzzy logic method for edge detection applied to color format images. The proposed algorithm combines the methodology based on the image gradients and general type-2 fuzzy logic theory to provide a powerful edge detection method. General type-2 fuzzy inference systems are approximated using the α-planes approach. The edge detection method is tested on a database of color images with the idea of illustrating the advantage of applying the fuzzy edge detection approach on color images against grayscale format images, and also when the images are corrupted by noise. This paper compares the proposed method based on general type-2 fuzzy logic with other edge detection algorithms, such as ones based on type-1 and interval type-2 fuzzy systems. Simulation results show that edge detection based on a general type-2 fuzzy system outperforms the other methods because of its ability to handle the intrinsic uncertainty in this problem. Full article
(This article belongs to the Special Issue Fuzzy Logic for Image Processing)
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Open AccessArticle Fuzzy Color Clustering for Melanoma Diagnosis in Dermoscopy Images
Information 2017, 8(3), 89; doi:10.3390/info8030089
Received: 20 May 2017 / Revised: 21 July 2017 / Accepted: 21 July 2017 / Published: 25 July 2017
Cited by 1 | PDF Full-text (3194 KB) | HTML Full-text | XML Full-text
Abstract
A fuzzy logic-based color histogram analysis technique is presented for discriminating benign skin lesions from malignant melanomas in dermoscopy images. The approach extends previous research for utilizing a fuzzy set for skin lesion color for a specified class of skin lesions, using alpha-cut
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A fuzzy logic-based color histogram analysis technique is presented for discriminating benign skin lesions from malignant melanomas in dermoscopy images. The approach extends previous research for utilizing a fuzzy set for skin lesion color for a specified class of skin lesions, using alpha-cut and support set cardinality for quantifying a fuzzy ratio skin lesion color feature. Skin lesion discrimination results are reported for the fuzzy clustering ratio over different regions of the lesion over a data set of 517 dermoscopy images consisting of 175 invasive melanomas and 342 benign lesions. Experimental results show that the fuzzy clustering ratio applied over an eight-connected neighborhood on the outer 25% of the skin lesion with an alpha-cut of 0.08 can recognize 92.6% of melanomas with approximately 13.5% false positive lesions. These results show the critical importance of colors in the lesion periphery. Our fuzzy logic-based description of lesion colors offers relevance to clinical descriptions of malignant melanoma. Full article
(This article belongs to the Special Issue Fuzzy Logic for Image Processing)
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Open AccessArticle Automated Prostate Gland Segmentation Based on an Unsupervised Fuzzy C-Means Clustering Technique Using Multispectral T1w and T2w MR Imaging
Information 2017, 8(2), 49; doi:10.3390/info8020049
Received: 4 February 2017 / Revised: 3 April 2017 / Accepted: 24 April 2017 / Published: 28 April 2017
Cited by 1 | PDF Full-text (4264 KB) | HTML Full-text | XML Full-text
Abstract
Prostate imaging analysis is difficult in diagnosis, therapy, and staging of prostate cancer. In clinical practice, Magnetic Resonance Imaging (MRI) is increasingly used thanks to its morphologic and functional capabilities. However, manual detection and delineation of prostate gland on multispectral MRI data is
[...] Read more.
Prostate imaging analysis is difficult in diagnosis, therapy, and staging of prostate cancer. In clinical practice, Magnetic Resonance Imaging (MRI) is increasingly used thanks to its morphologic and functional capabilities. However, manual detection and delineation of prostate gland on multispectral MRI data is currently a time-expensive and operator-dependent procedure. Efficient computer-assisted segmentation approaches are not yet able to address these issues, but rather have the potential to do so. In this paper, a novel automatic prostate MR image segmentation method based on the Fuzzy C-Means (FCM) clustering algorithm, which enables multispectral T1-weighted (T1w) and T2-weighted (T2w) MRI anatomical data processing, is proposed. This approach, using an unsupervised Machine Learning technique, helps to segment the prostate gland effectively. A total of 21 patients with suspicion of prostate cancer were enrolled in this study. Volume-based metrics, spatial overlap-based metrics and spatial distance-based metrics were used to quantitatively evaluate the accuracy of the obtained segmentation results with respect to the gold-standard boundaries delineated manually by an expert radiologist. The proposed multispectral segmentation method was compared with the same processing pipeline applied on either T2w or T1w MR images alone. The multispectral approach considerably outperforms the monoparametric ones, achieving an average Dice Similarity Coefficient 90.77 ± 1.75, with respect to 81.90 ± 6.49 and 82.55 ± 4.93 by processing T2w and T1w imaging alone, respectively. Combining T2w and T1w MR image structural information significantly enhances prostate gland segmentation by exploiting the uniform gray appearance of the prostate on T1w MRI. Full article
(This article belongs to the Special Issue Fuzzy Logic for Image Processing)
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Review

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Open AccessReview Review of Recent Type-2 Fuzzy Image Processing Applications
Information 2017, 8(3), 97; doi:10.3390/info8030097
Received: 30 June 2017 / Revised: 5 August 2017 / Accepted: 8 August 2017 / Published: 10 August 2017
Cited by 2 | PDF Full-text (2462 KB) | HTML Full-text | XML Full-text
Abstract
This paper presents a literature review of applications using type-2 fuzzy systems in the area of image processing. Over the last years, there has been a significant increase in research on higher-order forms of fuzzy logic; in particular, the use of interval type-2
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This paper presents a literature review of applications using type-2 fuzzy systems in the area of image processing. Over the last years, there has been a significant increase in research on higher-order forms of fuzzy logic; in particular, the use of interval type-2 fuzzy sets and general type-2 fuzzy sets. The idea of making use of higher orders, or types, of fuzzy logic is to capture and represent uncertainty that is more complex. This paper is focused on image processing systems, which includes image segmentation, image filtering, image classification and edge detection. Various applications are presented where general type-2 fuzzy sets, interval type-2 fuzzy sets, and interval-value fuzzy sets are used; some are compared with the traditional type-1 fuzzy sets and others methodologies that exist in the literature for these areas in image processing. In all accounts, it is shown that type-2 fuzzy sets outperform both traditional image processing techniques as well as techniques using type-1 fuzzy sets, and provide the ability to handle uncertainty when the image is corrupted by noise. Full article
(This article belongs to the Special Issue Fuzzy Logic for Image Processing)
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