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Ferrante di Ruffano L, Takwoingi Y, Dinnes J, Chuchu N, Bayliss SE, Davenport C, Matin RN, Godfrey K, O'Sullivan C, Gulati A, Chan SA, Durack A, O'Connell S, Gardiner MD, Bamber J, Deeks JJ, Williams HC. Computer-assisted diagnosis techniques (dermoscopy and spectroscopy-based) for diagnosing skin cancer in adults. Cochrane Database Syst Rev 2018; 12:CD013186. [PMID: 30521691 PMCID: PMC6517147 DOI: 10.1002/14651858.cd013186] [Citation(s) in RCA: 44] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
BACKGROUND Early accurate detection of all skin cancer types is essential to guide appropriate management and to improve morbidity and survival. Melanoma and cutaneous squamous cell carcinoma (cSCC) are high-risk skin cancers which have the potential to metastasise and ultimately lead to death, whereas basal cell carcinoma (BCC) is usually localised with potential to infiltrate and damage surrounding tissue. Anxiety around missing early curable cases needs to be balanced against inappropriate referral and unnecessary excision of benign lesions. Computer-assisted diagnosis (CAD) systems use artificial intelligence to analyse lesion data and arrive at a diagnosis of skin cancer. When used in unreferred settings ('primary care'), CAD may assist general practitioners (GPs) or other clinicians to more appropriately triage high-risk lesions to secondary care. Used alongside clinical and dermoscopic suspicion of malignancy, CAD may reduce unnecessary excisions without missing melanoma cases. OBJECTIVES To determine the accuracy of CAD systems for diagnosing cutaneous invasive melanoma and atypical intraepidermal melanocytic variants, BCC or cSCC in adults, and to compare its accuracy with that of dermoscopy. SEARCH METHODS We undertook a comprehensive search of the following databases from inception up to August 2016: Cochrane Central Register of Controlled Trials (CENTRAL); MEDLINE; Embase; CINAHL; CPCI; Zetoc; Science Citation Index; US National Institutes of Health Ongoing Trials Register; NIHR Clinical Research Network Portfolio Database; and the World Health Organization International Clinical Trials Registry Platform. We studied reference lists and published systematic review articles. SELECTION CRITERIA Studies of any design that evaluated CAD alone, or in comparison with dermoscopy, in adults with lesions suspicious for melanoma or BCC or cSCC, and compared with a reference standard of either histological confirmation or clinical follow-up. DATA COLLECTION AND ANALYSIS Two review authors independently extracted all data using a standardised data extraction and quality assessment form (based on QUADAS-2). We contacted authors of included studies where information related to the target condition or diagnostic threshold were missing. We estimated summary sensitivities and specificities separately by type of CAD system, using the bivariate hierarchical model. We compared CAD with dermoscopy using (a) all available CAD data (indirect comparisons), and (b) studies providing paired data for both tests (direct comparisons). We tested the contribution of human decision-making to the accuracy of CAD diagnoses in a sensitivity analysis by removing studies that gave CAD results to clinicians to guide diagnostic decision-making. MAIN RESULTS We included 42 studies, 24 evaluating digital dermoscopy-based CAD systems (Derm-CAD) in 23 study cohorts with 9602 lesions (1220 melanomas, at least 83 BCCs, 9 cSCCs), providing 32 datasets for Derm-CAD and seven for dermoscopy. Eighteen studies evaluated spectroscopy-based CAD (Spectro-CAD) in 16 study cohorts with 6336 lesions (934 melanomas, 163 BCC, 49 cSCCs), providing 32 datasets for Spectro-CAD and six for dermoscopy. These consisted of 15 studies using multispectral imaging (MSI), two studies using electrical impedance spectroscopy (EIS) and one study using diffuse-reflectance spectroscopy. Studies were incompletely reported and at unclear to high risk of bias across all domains. Included studies inadequately address the review question, due to an abundance of low-quality studies, poor reporting, and recruitment of highly selected groups of participants.Across all CAD systems, we found considerable variation in the hardware and software technologies used, the types of classification algorithm employed, methods used to train the algorithms, and which lesion morphological features were extracted and analysed across all CAD systems, and even between studies evaluating CAD systems. Meta-analysis found CAD systems had high sensitivity for correct identification of cutaneous invasive melanoma and atypical intraepidermal melanocytic variants in highly selected populations, but with low and very variable specificity, particularly for Spectro-CAD systems. Pooled data from 22 studies estimated the sensitivity of Derm-CAD for the detection of melanoma as 90.1% (95% confidence interval (CI) 84.0% to 94.0%) and specificity as 74.3% (95% CI 63.6% to 82.7%). Pooled data from eight studies estimated the sensitivity of multispectral imaging CAD (MSI-CAD) as 92.9% (95% CI 83.7% to 97.1%) and specificity as 43.6% (95% CI 24.8% to 64.5%). When applied to a hypothetical population of 1000 lesions at the mean observed melanoma prevalence of 20%, Derm-CAD would miss 20 melanomas and would lead to 206 false-positive results for melanoma. MSI-CAD would miss 14 melanomas and would lead to 451 false diagnoses for melanoma. Preliminary findings suggest CAD systems are at least as sensitive as assessment of dermoscopic images for the diagnosis of invasive melanoma and atypical intraepidermal melanocytic variants. We are unable to make summary statements about the use of CAD in unreferred populations, or its accuracy in detecting keratinocyte cancers, or its use in any setting as a diagnostic aid, because of the paucity of studies. AUTHORS' CONCLUSIONS In highly selected patient populations all CAD types demonstrate high sensitivity, and could prove useful as a back-up for specialist diagnosis to assist in minimising the risk of missing melanomas. However, the evidence base is currently too poor to understand whether CAD system outputs translate to different clinical decision-making in practice. Insufficient data are available on the use of CAD in community settings, or for the detection of keratinocyte cancers. The evidence base for individual systems is too limited to draw conclusions on which might be preferred for practice. Prospective comparative studies are required that evaluate the use of already evaluated CAD systems as diagnostic aids, by comparison to face-to-face dermoscopy, and in participant populations that are representative of those in which the test would be used in practice.
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Affiliation(s)
| | - Yemisi Takwoingi
- University of BirminghamInstitute of Applied Health ResearchEdgbaston CampusBirminghamUKB15 2TT
- University Hospitals Birmingham NHS Foundation Trust and University of BirminghamNIHR Birmingham Biomedical Research CentreBirminghamUK
| | - Jacqueline Dinnes
- University of BirminghamInstitute of Applied Health ResearchEdgbaston CampusBirminghamUKB15 2TT
- University Hospitals Birmingham NHS Foundation Trust and University of BirminghamNIHR Birmingham Biomedical Research CentreBirminghamUK
| | - Naomi Chuchu
- University of BirminghamInstitute of Applied Health ResearchEdgbaston CampusBirminghamUKB15 2TT
| | - Susan E Bayliss
- University of BirminghamInstitute of Applied Health ResearchEdgbaston CampusBirminghamUKB15 2TT
| | - Clare Davenport
- University of BirminghamInstitute of Applied Health ResearchEdgbaston CampusBirminghamUKB15 2TT
| | - Rubeta N Matin
- Churchill HospitalDepartment of DermatologyOld RoadHeadingtonOxfordUKOX3 7LE
| | - Kathie Godfrey
- The University of Nottinghamc/o Cochrane Skin GroupNottinghamUK
| | | | - Abha Gulati
- Barts Health NHS TrustDepartment of DermatologyWhitechapelLondonUKE11BB
| | - Sue Ann Chan
- City HospitalBirmingham Skin CentreDudley RdBirminghamUKB18 7QH
| | - Alana Durack
- Addenbrooke’s Hospital, Cambridge University Hospitals NHS Foundation TrustDermatologyHills RoadCambridgeUKCB2 0QQ
| | - Susan O'Connell
- Cardiff and Vale University Health BoardCEDAR Healthcare Technology Research CentreCardiff Medicentre, University Hospital of Wales, Heath Park CampusCardiffWalesUKCF144UJ
| | | | - Jeffrey Bamber
- Institute of Cancer Research and The Royal Marsden NHS Foundation TrustJoint Department of Physics15 Cotswold RoadSuttonUKSM2 5NG
| | - Jonathan J Deeks
- University of BirminghamInstitute of Applied Health ResearchEdgbaston CampusBirminghamUKB15 2TT
- University Hospitals Birmingham NHS Foundation Trust and University of BirminghamNIHR Birmingham Biomedical Research CentreBirminghamUK
| | - Hywel C Williams
- University of NottinghamCentre of Evidence Based DermatologyQueen's Medical CentreDerby RoadNottinghamUKNG7 2UH
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Sabbaghi Mahmouei S, Aldeen M, Stoecker WV, Garnavi R. Biologically Inspired QuadTree Color Detection in Dermoscopy Images of Melanoma. IEEE J Biomed Health Inform 2018; 23:570-577. [PMID: 29993590 DOI: 10.1109/jbhi.2018.2841428] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
This paper presents a QuadTree-based melanoma detection system inspired by dermatologists' color perception. Clinical color assessment in dermoscopy images is challenging because of subtle differences in shades, location-dependent color information, poor color contrast, and wide variation among images of the same class. To overcome these challenges, color enhancement and automatic color identification techniques, based on QuadTree segmentation and modeled after expert color assessments, are developed. The approach presented in this paper is shown to provide an accurate model of expert color assessment. Specifically, the proposed model is shown to: 1) identify significantly more colors in melanomas than in benign skin lesions; 2) identify a higher frequency in melanomas of three colors: blue-gray, black, and pink; and 3) delineate locations of melanoma colors by quintiles, specifically predilection for blue-gray and pink in the periphery and a trend for white and black in the lesion center. Performance of the proposed method is evaluated using four classifiers. The kernel support vector machine classifier is found to achieve the best results, with an area under the receiver operating characteristic (ROC) curve of 0.93, compared to average area under the ROC curve of 0.82 achieved by the dermatologists in this study. The results indicate that the biologically inspired method of automatic color detection proposed in this paper has the potential to play an important role in melanoma diagnosis in the clinic.
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A Review of Image Processing Techniques Common in Human and Plant Disease Diagnosis. Symmetry (Basel) 2018. [DOI: 10.3390/sym10070270] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022] Open
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Katapadi AB, Celebi ME, Trotter SC, Gurcan MN. Evolving strategies for the development and evaluation of a computerised melanoma image analysis system. COMPUTER METHODS IN BIOMECHANICS AND BIOMEDICAL ENGINEERING: IMAGING & VISUALIZATION 2017. [DOI: 10.1080/21681163.2016.1277785] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Affiliation(s)
- Aashish B. Katapadi
- Clinical Image Analysis Lab, Department of Biomedical Informatics, The Ohio State University, Columbus, OH, USA
| | - M. Emre Celebi
- Department of Computer Science, University of Central Arkansas, Conway, Akransas, USA
| | - Shannon C. Trotter
- Comprehensive Cancer Center, Arthur G. James Cancer Hospital and Richard J. Solove Research Institute, The Ohio State University Medical Center, Columbus, OH, USA
| | - Metin N. Gurcan
- Clinical Image Analysis Lab, Department of Biomedical Informatics, The Ohio State University, Columbus, OH, USA
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Computerized analysis of pigmented skin lesions: A review. Artif Intell Med 2012; 56:69-90. [DOI: 10.1016/j.artmed.2012.08.002] [Citation(s) in RCA: 238] [Impact Index Per Article: 19.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2012] [Revised: 08/02/2012] [Accepted: 08/19/2012] [Indexed: 11/20/2022]
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Stanley RJ, Stoecker WV, Moss RH, Rabinovitz HS, Cognetta AB, Argenziano G, Soyer HP. A basis function feature-based approach for skin lesion discrimination in dermatology dermoscopy images. Skin Res Technol 2008; 14:425-35. [PMID: 18937777 DOI: 10.1111/j.1600-0846.2008.00307.x] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
BACKGROUND Skin lesion color is an important feature for diagnosing malignant melanoma. New basis function correlation features are proposed for discriminating malignant melanoma lesions from benign lesions in dermoscopy images. The proposed features are computed based on correlating the luminance histogram of melanoma or benign labeled relative colors from a specified portion of the skin lesion with a set of basis functions. These features extend previously developed statistical and fuzzy logic-based relative color histogram analysis techniques for automated mapping of colors representative of melanoma and benign skin lesions from a training set of lesion images. METHODS Using the statistical and fuzzy logic-based approaches for relative color mapping, melanoma and benign color features are computed over skin lesion region of interest, respectively. Luminance histograms are obtained from the melanoma and benign mapped colors within the lesion region of interest and are correlated with a set of basis functions to quantify the distribution of colors. The histogram analysis techniques and feature calculations are evaluated using a data set of 279 malignant melanomas and 442 benign dysplastic nevi images. RESULTS Experimental test results showed that combining existing melanoma and benign color features with the proposed basis function features found from the melanoma mapped colors yielded average correct melanoma and benign lesion discrimination rates as high as 86.45% and 83.35%, respectively. CONCLUSIONS The basis function features provide an alternative approach to melanoma discrimination that quantifies the variation and distribution of colors characteristic of melanoma and benign skin lesions.
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Affiliation(s)
- R Joe Stanley
- Department of Electrical and Computer Engineering, Missouri University of Science and Technology, Rolla, MO 65409-0040, USA.
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Langley RGB, Burton E, Walsh N, Propperova I, Murray SJ. In vivo confocal scanning laser microscopy of benign lentigines: Comparison to conventional histology and in vivo characteristics of lentigo maligna. J Am Acad Dermatol 2006; 55:88-97. [PMID: 16781299 DOI: 10.1016/j.jaad.2006.03.009] [Citation(s) in RCA: 99] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2005] [Revised: 02/23/2006] [Accepted: 03/13/2006] [Indexed: 11/24/2022]
Abstract
BACKGROUND An important challenge facing clinicians is recognizing and distinguishing benign pigmented lesions from cutaneous melanoma. Lentigines are a type of benign pigmented lesion that can resemble melanoma. Physician diagnostic accuracy is less than perfect, prompting research into noninvasive technology such as reflectance mode in vivo confocal scanning laser microscopy (CSLM). OBJECTIVES Our aims were twofold: to describe the in vivo characteristics of benign lentigines with reflectance CSLM and to compare them with histopathology; and to contrast the in vivo CSLM differences of lentigines, lentigo maligna, and lentigo maligna melanomas. METHODS Patients with a suspect pigmented lesion were prospectively recruited to undergo CSLM before biopsy. Lentigo simplex, solar lentigo, or malignant melanoma, lentigo maligna type, were included in the study. Images were qualitatively described and compared with histopathologic findings. RESULTS Ten patients, whose lesions included 6 lentigines and 4 lentigo malignas, were examined with CSLM. Distinct architectural and cytologic features were noted in benign lentigines compared with melanomas. The most striking finding in lentigines was observed at the dermoepidermal junction. In all cases of lentigines there was an increase in the density of dermal papillae surrounded by a bright monomorphic layer of cells. Distinct patterns were noted, as these papillae assumed irregular geometric shapes or formed papillary projections with a rim of bright, highly refractile, monomorphic, and cytologically benign-appearing cells. These findings were absent in all of the melanomas studied. Lentigines had an absence of atypical melanocytes, whereas the melanomas had bright, atypical, polymorphous cells present in a pagetoid pattern with coarse, branching dendrites observed throughout the epidermis. LIMITATIONS This is a descriptive pilot study involving a limited number of patients. CONCLUSION Unique CSLM characteristics of lentigines were found that have not been previously described, facilitating rapid in vivo discrimination from malignant melanoma. This descriptive study supports the further examination of CSLM features of lentigines to aid in the diagnosis of melanoma and discrimination from benign lesions.
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Affiliation(s)
- Richard G B Langley
- QEII Health Sciences Centre, Division of Dermatology, Department of Medicine, Dalhousie University, Halifax, Nova Scotia, Canada.
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Chang Y, Stanley RJ, Moss RH, Van Stoecker W. A systematic heuristic approach for feature selection for melanoma discrimination using clinical images. Skin Res Technol 2005; 11:165-78. [PMID: 15998327 PMCID: PMC3193077 DOI: 10.1111/j.1600-0846.2005.00116.x] [Citation(s) in RCA: 11] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
Abstract
BACKGROUND Numerous features are derived from the asymmetry, border irregularity, color variegation, and diameter of the skin lesion in dermatology for diagnosing malignant melanoma. Feature selection for the development of automated skin lesion discrimination systems is an important consideration. METHODS In this research, a systematic heuristic approach is investigated for feature selection and lesion classification. The approach integrates statistical-, correlation-, histogram-, and expert system-based components. Using statistical and correlation measures, interrelationships among features are determined. Expert system analysis is performed to identify redundant features. The feature selection process is applied to 19 shape and color features for a clinical image data set containing 355 malignant melanomas, 125 basal cell carcinomas, 177 dysplastic nevi, 199 nevocellular nevi, 139 seborrheic keratoses, and 45 vascular lesions. RESULTS Experimental results show reduced lesion classification error rates based on condensing the shape and color feature set from 19 features to 13 features using the feature selection process. Specifically, average test lesion classification error rates for discriminating malignant melanoma from non-melanoma lesions were reduced from 26.6% for 19 features to 23.2% for 13 features over five randomly generated training and test sets. CONCLUSIONS The experimental results show that the systematic heuristic approach for feature reduction can be successfully applied to achieve improved lesion discrimination. The feature reduction technique facilitates the elimination of redundant information that may inhibit lesion classification performance. The clinical application of this result is that automated skin lesion classification algorithm development can be fostered with systematic feature selection techniques.
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Affiliation(s)
- Ying Chang
- Department of Electrical and Computer Engineering, University of Missouri-Rolla, Hall Rolla, MO 65409-0040, USA.
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Stanley RJ, Moss RH, Van Stoecker W, Aggarwal C. A fuzzy-based histogram analysis technique for skin lesion discrimination in dermatology clinical images. Comput Med Imaging Graph 2003; 27:387-96. [PMID: 12821032 PMCID: PMC3184460 DOI: 10.1016/s0895-6111(03)00030-2] [Citation(s) in RCA: 51] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
A fuzzy logic-based color histogram analysis technique is presented for discriminating benign skin lesions from malignant melanomas in dermatology clinical images. The approach utilizes a fuzzy set for benign skin lesion color, and alpha-cut and support set cardinality for quantifying a fuzzy ratio skin lesion color feature. Skin lesion discrimination results are reported for the fuzzy ratio and fusion with a previously determined percent melanoma color feature over a data set of 258 clinical images. For the fusion technique, alpha-cuts for the fuzzy ratio can be chosen to recognize over 93.30% of melanomas with approximately 15.67% false positive lesions.
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Affiliation(s)
- R Joe Stanley
- Department of Electrical and Computer Engineering, University of Missouri-Rolla, 1870 Miner Circle, 127 Emerson Electric Company Hall, Rolla, MO 65409, USA.
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Faziloglu Y, Stanley RJ, Moss RH, Van Stoecker W, McLean RP. Colour histogram analysis for melanoma discrimination in clinical images. Skin Res Technol 2003; 9:147-56. [PMID: 12709133 PMCID: PMC3191539 DOI: 10.1034/j.1600-0846.2003.00030.x] [Citation(s) in RCA: 32] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
BACKGROUND Malignant melanoma, the most deadly form of skin cancer, has a good prognosis if treated in the curable early stages. Colour provides critical discriminating information for the diagnosis of malignant melanoma. METHODS This research introduces a three-dimensional relative colour histogram analysis technique to identify colours characteristic of melanomas and then applies these 'melanoma colours' to differentiate benign skin lesions from melanomas. The relative colour of a skin lesion is determined based on subtracting a representative colour of the surrounding skin from each lesion pixel. A colour mapping for 'melanoma colours' is determined using a training set of images. A percent melanoma colour feature, defined as the percentage of the lesion pixels that are melanoma colours, is used for discriminating melanomas from benign lesions. The technique is evaluated using a clinical image data set of 129 malignant melanomas and 129 benign lesions consisting of 40 seborrheic keratoses and 89 nevocellular nevi. RESULTS Using the percent melanoma colour feature for discrimination, experimental results yield correct melanoma and benign lesion discrimination rates of 84.3 and 83.0%, respectively. CONCLUSIONS The results presented in this work suggest that lesion colour in clinical images is strongly related to the presence of melanoma in that lesion. However, colour information should be combined with other information in order to further reduce the false negative and false positive rates.
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Affiliation(s)
- Yunus Faziloglu
- University of Missouri-Rolla, Department of Electrical and Computer Engineering, 229 Emerson Electric Co, Hall, MO 65409-0040, USA
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Chen J, Stanley RJ, Moss RH, Van Stoecker W. Colour analysis of skin lesion regions for melanoma discrimination in clinical images. Skin Res Technol 2003; 9:94-104. [PMID: 12709126 PMCID: PMC3196565 DOI: 10.1034/j.1600-0846.2003.00024.x] [Citation(s) in RCA: 24] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
BACKGROUND Skin lesion colour is an important feature for diagnosing malignant melanoma. Colour histogram analysis over a training set of images has been used to identify colours characteristic of melanoma, i.e., melanoma colours. A percent melanoma colour feature defined as the percentage of the lesion pixels that are melanoma colours has been used as a feature to discriminate melanomas from benign lesions. METHODS In this research, the colour histogram analysis technique is extended to evaluate skin lesion discrimination based on colour feature calculations in different regions of the skin lesion. The colour features examined include percent melanoma colour and a novel colour clustering ratio. Experiments are performed using clinical images of 129 malignant melanomas and 129 benign lesions consisting of 40 seborrheic keratoses and 89 nevocellular nevi. RESULTS Experimental results show improved discrimination capability for feature calculations focused in the lesion boundary region. Specifically, correct melanoma and benign recognition rates are observed as high as 89 and 83%, respectively, for the percent melanoma colour feature computed using only the outermost, uniformly distributed 10% of the lesion's area. CONCLUSIONS The experimental results show for the features investigated that the region closest to the skin lesion boundary contains the greatest colour discrimination information for lesion screening. Furthermore, the percent melanoma colour feature consistently outperformed the colour clustering ratio for the different skin lesion regions examined. The clinical application of this result is that clustered colours appear to be no more significant than colours of arbitrary distribution within a lesion.
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Affiliation(s)
- Jixiang Chen
- Department of Electrical and Computer Engineering, University of Missouri-Rolla, 229 Emerson Electric Co. Hall, Rolla, MO 65409-0040, USA and
| | - R. Joe Stanley
- Department of Electrical and Computer Engineering, University of Missouri-Rolla, 229 Emerson Electric Co. Hall, Rolla, MO 65409-0040, USA and
| | - Randy H. Moss
- Department of Electrical and Computer Engineering, University of Missouri-Rolla, 229 Emerson Electric Co. Hall, Rolla, MO 65409-0040, USA and
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Affiliation(s)
- Sarah Brenner
- Department of Dermatology, Tel Aviv Sourasky Medical Center and Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel.
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Abstract
Automated melanoma diagnosis is a popular focus of research, with numerous papers describing techniques and results. In our study, we identified two possible problems with the current method of automated diagnosis, where systems are intended to reproduce histopathology results. We propose a new method of identifying problematic skin lesions, namely attempting to reproduce algorithmically the perceptions of dermatologists as to whether the lesion should be excised. In the best case, our initial model reproduced the decision of dermatologists in over 80% of cases. These results suggest that reproducing the decision to excise may be a valuable adjunct to current methodology.
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Affiliation(s)
- G R Day
- Department of Computer Science, University of Waikato, Hamilton, New Zealand
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Lentini A, Autuori F, Mattioli P, Caraglia M, Abbruzzese A, Beninati S. Evaluation of the efficacy of potential antineoplastic drugs on tumour metastasis by a computer-assisted image analysis. Eur J Cancer 2000; 36:1572-7. [PMID: 10930806 DOI: 10.1016/s0959-8049(00)00147-7] [Citation(s) in RCA: 20] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Computerised image analysis, performed on histological sections of (C57BL6/N) mouse lungs that had been intravenously (i.v.) injected with B16-F10 melanoma cells was used to develop a novel method to quantify the efficacy of potential antineoplastic drugs. This procedure allowed the evaluation of the rate of inhibition of growth and the anti-invasive capability of new molecules, thus resulting in more accurate data than that obtained from common macroscopical counting of surface metastatic foci. Several morphological parameters can be measured by this method: the percentage of tissue area occupied by metastases, which accounts for tumour implantation into the organ; the growth index, related to the size of the metastases, and the invasion index, related to the frequency of foci. These morphometric data were found to be correlated to the levels of lung hydroxyproline and transglutaminase activity, well known markers of tumour invasion and cell differentiation, respectively. The main objective of this computerised procedure was to evaluate how the tumour cell is affected in the host by the drug under investigation. The use of the method is exemplified by an analysis of the antitumour activity of some methylxanthines.
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Affiliation(s)
- A Lentini
- Department of Biology, University of Rome 'Tor Vergata' Via della Ricerca Scientifica, 00133, Rome, Italy
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