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Combined Spline and B-spline for an improved automatic skin lesion segmentation in dermoscopic images using optimal color channel

Abbas AA, Guo X, Tan WH, Jalab HA

J Med Syst, 2014 Aug;38(8):80.
PMID: 24957396 DOI: 10.1007/s10916-014-0080-7

In a computerized image analysis environment, the irregularity of a lesion border has been used to differentiate between malignant melanoma and other pigmented skin lesions. The accuracy of the automated lesion border detection is a significant step towards accurate classification at a later stage. In this paper, we propose the use of a combined Spline and B-spline in order to enhance the quality of dermoscopic images before segmentation. In this paper, morphological operations and median filter were used first to remove noise from the original image during pre-processing. Then we proceeded to adjust image RGB values to the optimal color channel (green channel). The combined Spline and B-spline method was subsequently adopted to enhance the image before segmentation. The lesion segmentation was completed based on threshold value empirically obtained using the optimal color channel. Finally, morphological operations were utilized to merge the smaller regions with the main lesion region. Improvement on the average segmentation accuracy was observed in the experimental results conducted on 70 dermoscopic images. The average accuracy of segmentation achieved in this paper was 97.21 % (where, the average sensitivity and specificity were 94 % and 98.05 % respectively).

Matched MeSH terms: Dermoscopy/methods*
Bluish veil detection and lesion classification using custom deep learnable layers with explainable artificial intelligence (XAI)

Rasel MA, Abdul Kareem S, Kwan Z, Yong SS, Obaidellah U

Comput Biol Med, 2024 Aug;178:108758.
PMID: 38905895 DOI: 10.1016/j.compbiomed.2024.108758

Melanoma, one of the deadliest types of skin cancer, accounts for thousands of fatalities globally. The bluish, blue-whitish, or blue-white veil (BWV) is a critical feature for diagnosing melanoma, yet research into detecting BWV in dermatological images is limited. This study utilizes a non-annotated skin lesion dataset, which is converted into an annotated dataset using a proposed imaging algorithm (color threshold techniques) on lesion patches based on color palettes. A Deep Convolutional Neural Network (DCNN) is designed and trained separately on three individual and combined dermoscopic datasets, using custom layers instead of standard activation function layers. The model is developed to categorize skin lesions based on the presence of BWV. The proposed DCNN demonstrates superior performance compared to the conventional BWV detection models across different datasets. The model achieves a testing accuracy of 85.71 % on the augmented PH2 dataset, 95.00 % on the augmented ISIC archive dataset, 95.05 % on the combined augmented (PH2+ISIC archive) dataset, and 90.00 % on the Derm7pt dataset. An explainable artificial intelligence (XAI) algorithm is subsequently applied to interpret the DCNN's decision-making process about the BWV detection. The proposed approach, coupled with XAI, significantly improves the detection of BWV in skin lesions, outperforming existing models and providing a robust tool for early melanoma diagnosis.

Matched MeSH terms: Dermoscopy/methods
Integrating color histogram analysis and convolutional neural networks for skin lesion classification

Rasel MA, Kareem SA, Obaidellah U

Comput Biol Med, 2024 Dec;183:109250.
PMID: 39395346 DOI: 10.1016/j.compbiomed.2024.109250

The color of skin lesions is a crucial diagnostic feature for identifying malignant melanoma and other skin diseases. Typical colors associated with melanocytic lesions include tan, brown, black, red, white, and blue-gray. This study introduces a novel feature: the number of colors present in lesions, which can indicate the severity of skin diseases and help distinguish melanomas from benign lesions. We propose a color histogram analysis, a traditional image processing technique, to analyze the pixels of skin lesions from three publicly available datasets: PH2, ISIC2016, and Med-Node, which include dermoscopic and non-dermoscopic images. While the PH2 dataset contains ground truth about skin lesion colors, the ISIC2016 and Med-Node datasets lack such annotations; our algorithm establishes this ground truth using the color histogram analysis based on the PH2 dataset. We then design and train a 19-layer Convolutional Neural Network (CNN) with different skip connections of residual blocks to classify lesions into three categories based on the number of colors present. The DeepDream algorithm is utilized to visualize the learned features of different layers, and multiple configurations of the proposed CNN are tested, achieving the highest weighted F1-score of 75.00 % on the test set. LIME is subsequently applied to identify the most important features influencing the model's decision-making. The findings demonstrate that the number of colors in lesions is a significant feature for describing skin conditions. The proposed CNN, particularly with three skip connections, shows strong potential for clinical application in diagnosing melanoma, supporting its use alongside traditional diagnostic methods.

Matched MeSH terms: Dermoscopy/methods
Fulltext An Adaptive Deep Learning Framework for Dynamic Image Classification in the Internet of Things Environment

Jameel SM, Hashmani MA, Rehman M, Budiman A

Sensors (Basel), 2020 Oct 14;20(20).
PMID: 33066579 DOI: 10.3390/s20205811

In the modern era of digitization, the analysis in the Internet of Things (IoT) environment demands a brisk amalgamation of domains such as high-dimension (images) data sensing technologies, robust internet connection (4 G or 5 G) and dynamic (adaptive) deep learning approaches. This is required for a broad range of indispensable intelligent applications, like intelligent healthcare systems. Dynamic image classification is one of the major areas of concern for researchers, which may take place during analysis under the IoT environment. Dynamic image classification is associated with several temporal data perturbations (such as novel class arrival and class evolution issue) which cause a massive classification deterioration in the deployed classification models and make them in-effective. Therefore, this study addresses such temporal inconsistencies (novel class arrival and class evolution issue) and proposes an adapted deep learning framework (ameliorated adaptive convolutional neural network (CNN) ensemble framework), which handles novel class arrival and class evaluation issue during dynamic image classification. The proposed framework is an improved version of previous adaptive CNN ensemble with an additional online training (OT) and online classifier update (OCU) modules. An OT module is a clustering-based approach which uses the Euclidean distance and silhouette method to determine the potential new classes, whereas, the OCU updates the weights of the existing instances of the ensemble with newly arrived samples. The proposed framework showed the desirable classification improvement under non-stationary scenarios for the benchmark (CIFAR10) and real (ISIC 2019: Skin disease) data streams. Also, the proposed framework outperformed against state-of-art shallow learning and deep learning models. The results have shown the effectiveness and proven the diversity of the proposed framework to adapt the new concept changes during dynamic image classification. In future work, the authors of this study aim to develop an IoT-enabled adaptive intelligent dermoscopy device (for dermatologists). Therefore, further improvements in classification accuracy (for real dataset) is the future concern of this study.

Matched MeSH terms: Dermoscopy
Novel techniques for enhancement and segmentation of acne vulgaris lesions

Malik AS, Humayun J, Kamel N, Yap FB

Skin Res Technol, 2014 Aug;20(3):322-31.
PMID: 24329769 DOI: 10.1111/srt.12122

BACKGROUND: More than 99% acne patients suffer from acne vulgaris. While diagnosing the severity of acne vulgaris lesions, dermatologists have observed inter-rater and intra-rater variability in diagnosis results. This is because during assessment, identifying lesion types and their counting is a tedious job for dermatologists. To make the assessment job objective and easier for dermatologists, an automated system based on image processing methods is proposed in this study.
OBJECTIVES: There are two main objectives: (i) to develop an algorithm for the enhancement of various acne vulgaris lesions; and (ii) to develop a method for the segmentation of enhanced acne vulgaris lesions.
METHODS: For the first objective, an algorithm is developed based on the theory of high dynamic range (HDR) images. The proposed algorithm uses local rank transform to generate the HDR images from a single acne image followed by the log transformation. Then, segmentation is performed by clustering the pixels based on Mahalanobis distance of each pixel from spectral models of acne vulgaris lesions.
RESULTS: Two metrics are used to evaluate the enhancement of acne vulgaris lesions, i.e., contrast improvement factor (CIF) and image contrast normalization (ICN). The proposed algorithm is compared with two other methods. The proposed enhancement algorithm shows better result than both the other methods based on CIF and ICN. In addition, sensitivity and specificity are calculated for the segmentation results. The proposed segmentation method shows higher sensitivity and specificity than other methods.
CONCLUSION: This article specifically discusses the contrast enhancement and segmentation for automated diagnosis system of acne vulgaris lesions. The results are promising that can be used for further classification of acne vulgaris lesions for final grading of the lesions.
KEYWORDS: acne grading; acne lesions; acne vulgaris; enhancement; segmentation

Matched MeSH terms: Dermoscopy/methods*
Computerised image analysis of vitiligo lesion: evaluation using manually defined lesion areas

Nugroho H, Ahmad Fadzil MH, Shamsudin N, Hussein SH

Skin Res Technol, 2013 Feb;19(1):e72-7.
PMID: 22233154 DOI: 10.1111/j.1600-0846.2011.00610.x

Vitiligo is a cutaneous pigmentary disorder characterized by depigmented macules and patches that result from loss of epidermal melanocytes. Physician evaluates the efficacy of treatment by comparing the extent of vitiligo lesions before and after treatment based on the overall visual impression of the treatment response. This method is called the physician's global assessment (PGA) which is subjective. In this article, we present an innovative digital image processing method to determine vitiligo lesion area in an objective manner.

Matched MeSH terms: Dermoscopy/methods*
Fulltext Acne analysis, grading and computational assessment methods: an overview

Ramli R, Malik AS, Hani AF, Jamil A

Skin Res Technol, 2012 Feb;18(1):1-14.
PMID: 21605170 DOI: 10.1111/j.1600-0846.2011.00542.x

INTRODUCTION: This paper presents a comprehensive review of acne grading and measurement. Acne is a chronic disorder of the pilosebaceous units, with excess sebum production, follicular epidermal hyperproliferation, inflammation and Propionibacterium acnes activity. Most patients are affected with acne vulgaris, which is the prevalent type of acne. Acne vulgaris consists of comedones (whitehead and blackhead), papules, pustules, nodules and cysts.
OBJECTIVES: To review and identify the issues for acne vulgaris grading and computational assessment methods. To determine the future direction for addressing the identified issues.
METHODS: There are two main methods of assessment for acne severity grading, namely, lesion counting and comparison of patient with a photographic standard. For the computational assessment method, the emphasis is on computational imaging techniques.
RESULTS: Current acne grading methods are very time consuming and tedious. Generally, they rely on approximation for counting lesions and hence the assessment is quite subjective, with both inter and intra-observer variability. It is important to accurately assess acne grade to evaluate its severity as this influences treatment selection and assessment of response to therapy. This will further help in better disease management and more efficacious treatment.
CONCLUSION: Semi-automated or automated methods based on computational imaging techniques should be devised for acne grade assessment.

Matched MeSH terms: Dermoscopy/methods*
Structural elucidation of peloton lysis using epi-fluorescence dyes

Senthilkumar S

Med J Malaysia, 2004 May;59 Suppl B:218-9.
PMID: 15468896
Matched MeSH terms: Dermoscopy
Fulltext Tinea corporis: an updated review

Leung AK, Lam JM, Leong KF, Hon KL

Drugs Context, 2020;9.
PMID: 32742295 DOI: 10.7573/dic.2020-5-6

Background: Tinea corporis is a common fungal infection that mimics many other annular lesions. Physicians must familiarize themselves with this condition and its treatment.
Objective: This article aimed to provide a narrative updated review on the evaluation, diagnosis, and treatment of tinea corporis.
Methods: A PubMed search was performed with Clinical Queries using the key term 'tinea corporis.' The search strategy included clinical trials, meta-analyses, randomized controlled trials, observational studies, and reviews. The search was restricted to the English language. The information retrieved from the mentioned search was used in the compilation of the present article.
Results: Tinea corporis typically presents as a well-demarcated, sharply circumscribed, oval or circular, mildly erythematous, scaly patch or plaque with a raised leading edge. Mild pruritus is common. The diagnosis is often clinical but can be difficult with prior use of medications, such as calcineurin inhibitors or corticosteroids. Dermoscopy is a useful and non-invasive diagnostic tool. If necessary, the diagnosis can be confirmed by microscopic examination of potassium hydroxide wet-mount preparations of skin scrapings from the active border of the lesion. Fungal culture is the gold standard to diagnose dermatophytosis especially if the diagnosis is in doubt and results of other tests are inconclusive or the infection is widespread, severe, or resistant to treatment. The standard treatment of tinea corporis is with topical antifungals. Systemic antifungal treatment is indicated if the lesion is multiple, extensive, deep, recurrent, chronic, or unresponsive to topical antifungal treatment, or if the patient is immunodeficient.
Conclusion: The diagnosis of tinea corporis is usually clinical and should pose no problem to the physician provided the lesion is typical. However, many clinical variants of tinea corporis exist, rendering the diagnosis difficult especially with prior use of medications, such as calcineurin inhibitors or corticosteroids. As such, physicians must be familiar with this condition so that an accurate diagnosis can be made and appropriate treatment initiated.

Matched MeSH terms: Dermoscopy
Area assessment of psoriasis lesion for PASI scoring

Ihtatho D, Fadzil MH, Affandi AM, Hussein SH

Annu Int Conf IEEE Eng Med Biol Soc, 2007;2007:3446-9.
PMID: 18002738

Psoriasis is a skin disorder which is caused by genetic fault. There is no cure for psoriasis, however, there are many treatment modalities to help control the disease. To evaluate treatment efficacy, PASI (Psoriasis Area and Severity Index) which is the current gold standard method is used to measure psoriasis severity by evaluating the area, erythema, scaliness and thickness of the plaques. However, the calculation of PASI can be tedious and subjective. In this work, we develop a computer vision method that determines one of the PASI parameter, the lesion area. The method isolates healthy (or healed) skin areas from lesion areas by analyzing the hue and chroma information in the CIE L*a*b* colour space. Centroids of healthy skin and psoriasis in the hue-chroma space are determined from selected sample. Euclidean distance of all pixels from each centroid is calculated. Each pixel is assigned to the class with minimum Euclidean distance. The study involves patients from three different ethnic origins having different skin tones. Results obtained show that the proposed method is comparable to the dermatologist visual approach.

Matched MeSH terms: Dermoscopy/methods*
Determination of skin repigmentation progression

Nugroho H, Fadzil MH, Yap VV, Norashikin S, Suraiya HH

Annu Int Conf IEEE Eng Med Biol Soc, 2007;2007:3442-5.
PMID: 18002737

In this paper, we describe an image processing scheme to analyze and determine areas of skin that have undergone repigmentation in particular, during the treatment of vitiligo. In vitiligo cases, areas of skin become pale or white due to the lack of skin pigment called melanin. Vitiligo treatment causes skin repigmentation resulting in a normal skin color. However, it is difficult to determine and quantify the amount of repigmentation visually during treatment because the repigmentation progress is slow and moreover changes in skin color can only be discerned over a longer time frame typically 6 months. Here, we develop a digital image analysis scheme that can identify and determine vitiligo skin areas and repigmentation progression on a shorter time period. The technique is based on principal component analysis and independent component analysis which converts the RGB skin image into a skin image that represent skin areas due to melanin and haemoglobin only, followed by segmentation process. Vitiligo skin lesions are identified as skin areas that lack melanin (non-melanin areas). In the initial studies of 4 patients, the method has been able to quantify repigmentation in vitiligo lesion. Hence it is now possible to determine repigmentation progression objectively and treatment efficacy on a shorter time cycle.

Matched MeSH terms: Dermoscopy/methods*
Body surface area measurement and soft clustering for PASI area assessment

Hani AF, Prakasa E, Nugroho H, Affandi AM, Hussein SH

Annu Int Conf IEEE Eng Med Biol Soc, 2012;2012:4398-401.
PMID: 23366902 DOI: 10.1109/EMBC.2012.6346941

Psoriasis is a common skin disorder with a prevalence of 0.6 - 4.8% around the world. The most common is plaques psoriasis and it appears as red scaling plaques. Psoriasis is incurable but treatable in a long term treatment. Although PASI (Psoriasis Area and Severity Index) scoring is recognised as gold standard for psoriasis assessment, this method is still influenced by inter and intra-rater variation. An imaging and analysis system called α-PASI is developed to perform PASI scoring objectively. Percentage of lesion area to the body surface area is one of PASI parameter. In this paper, enhanced imaging methods are developed to improve the determination of body surface area (BSA) and lesion area. BSA determination method has been validated on medical mannequin. BSA accuracies obtained at four body regions are 97.80% (lower limb), 92.41% (trunk), 87.72% (upper limb), and 83.82% (head). By applying fuzzy c-means clustering algorithm, the membership functions of lesions area for PASI area scoring have been determined. Performance of scoring result has been tested with double assessment by α-PASI area algorithm on body region images from 46 patients. Kappa coefficients for α-PASI system are greater than or equal to 0.72 for all body regions (Head - 0.76, Upper limb - 0.81, Trunk - 0.85, Lower limb - 0.72). The overall kappa coefficient for the α-PASI area is 0.80 that can be categorised as substantial agreement. This shows that the α-PASI area system has a high reliability and can be used in psoriasis area assessment.

Matched MeSH terms: Dermoscopy/methods*
Tinea Capitis: An Updated Review

Leung AKC, Hon KL, Leong KF, Barankin B, Lam JM

Recent Pat Inflamm Allergy Drug Discov, 2020;14(1):58-68.
PMID: 31906842 DOI: 10.2174/1872213X14666200106145624

BACKGROUND: Tinea capitis is a common and, at times, difficult to treat, fungal infection of the scalp.
OBJECTIVE: This article aimed to provide an update on the evaluation, diagnosis, and treatment of tinea capitis.
METHODS: A PubMed search was performed in Clinical Queries using the key term "tinea capitis". The search strategy included meta-analyses, randomized controlled trials, clinical trials, observational studies, and reviews. The search was restricted to English literature. The information retrieved from the above search was used in the compilation of the present article. Patents were searched using the key term "tinea capitis" at www.freepatentsonline.com.
RESULTS: Tinea capitis is most often caused by Trichophyton tonsurans and Microsporum canis. The peak incidence is between 3 and 7 years of age. Non-inflammatory tinea capitis typically presents as fine scaling with single or multiple scaly patches of circular alopecia (grey patches); diffuse or patchy, fine, white, adherent scaling of the scalp resembling generalized dandruff with subtle hair loss; or single or multiple patches of well-demarcated area (s) of alopecia with fine-scale, studded with broken-off hairs at the scalp surface, resulting in the appearance of "black dots". Inflammatory variants of tinea capitis include kerion and favus. Dermoscopy is a highly sensitive tool for the diagnosis of tinea capitis. The diagnosis can be confirmed by direct microscopic examination with a potassium hydroxide wetmount preparation and fungal culture. It is desirable to have mycologic confirmation of tinea capitis before beginning a treatment regimen. Oral antifungal therapy (terbinafine, griseofulvin, itraconazole, and fluconazole) is considered the gold standard for tinea capitis. Recent patents related to the management of tinea capitis are also discussed.
CONCLUSION: Tinea capitis requires systemic antifungal treatment. Although topical antifungal therapies have minimal adverse events, topical antifungal agents alone are not recommended for the treatment of tinea capitis because these agents do not penetrate the root of the hair follicles deep within the dermis. Topical antifungal therapy, however, can be used to reduce transmission of spores and can be used as adjuvant therapy to systemic antifungals. Combined therapy with topical and oral antifungals may increase the cure rate.

Matched MeSH terms: Dermoscopy*
Fulltext More than meets the naked eye: an unusual psoriatic arthritis mimicry and the important role of dermoscopic examination

Anne LJ, Rahim MJC, Ghazali WSW, Ahmed WAW, Isa SAM

BMC Rheumatol, 2021 Apr 12;5(1):10.
PMID: 33840385 DOI: 10.1186/s41927-021-00182-7

BACKGROUND: Psoriatic arthritis (PsA) can manifest in various forms. This includes mimicry of other diseases. We describe an unusual mimicry of PsA.
CASE PRESENTATION: We report a case of a middle-aged lady who presented with severe pain and morning stiffness over the small joints of the left hand for 3 months and painless deformity of the affected joints 1 year before. She was under treatment for pruritic rash over her ankles and knees for the past 1 year as well. Physical examination revealed a fixed flexion deformity, swelling and tenderness of the left ring and little fingers' distal interphalangeal (DIP) joints. Left hand radiograph showed sclerotic joint margin, narrowed joint space and marginal osteophytes of the affected DIP joints. Dermoscopic examination showed red- violaceous, flat-topped papules and plaques with minimal scales on both ankles; hyperpigmented scaly plaques over both knees and vertical fingernail ridges. Serum autoimmune screening and inflammatory markers were unremarkable. Left ankle skin biopsy showed features consistent of psoriasis. PsA was diagnosed. Weekly titrated oral methotrexate and topical steroid were started. The patient showed significant improvement after 1 month of treatment.
CONCLUSION: PsA is a great mimicker. Dermoscopy is an accessible and valuable tool to assess skin lesions in greater detail. Clinicians should be aware of coexisting diseases or misdiagnosis when patients do not respond to treatment.

Matched MeSH terms: Dermoscopy
Melanonychia striata: clarifying behind the Black Curtain. A review on clinical evaluation and management of the 21st century

Leung AKC, Lam JM, Leong KF, Sergi CM

Int J Dermatol, 2019 Nov;58(11):1239-1245.
PMID: 31006857 DOI: 10.1111/ijd.14464

Melanonychia striata is characterized by a tan, brown, or black longitudinal streak within the nail plate that runs from the proximal nail fold to the distal part of the nail plate. Melanonychia striata is due to increased activity of melanocytes or melanocytic hyperplasia in the nail matrix with subsequently increased melanin deposition in the nail plate. The most common cause of melanonychia striata associated with melanocytic activation is ethnic melanonychia which occurs in dark-skinned individuals. Other causes of melanonychia striata related to melanocytic activation include pregnancy, chronic local trauma, infections, medications, dermatological disorders, endocrine disorders, alkaptonuria, hemochromatosis, porphyria, graft-vs-host disease, Peutz-Jeghers syndrome, and Laugier-Hunziker syndrome. Causes of melanonychia striata associated with melanocytic hyperplasia include nail matrix melanocytic nevus, nail lentigo, and nail apparatus/subungual in situ and invasive melanoma. In most cases, melanonychia striata is a benign condition, especially in children. Consequently, most investigators advocate a wait-and-see approach. Nail apparatus/subungual melanoma should be suspected if there is an abrupt onset after middle age, personal or family history of melanoma, rapid growth, darkening of a melanonychia band, pigment variegation, blurry lateral borders, irregular elevation of the surface, a bandwidth >3 mm, proximal widening, associated nail plate dystrophy, single rather than multiple digit involvement, and periungual spread of pigmentation onto the adjacent cuticle and/or proximal and/or lateral nail folds (Hutchinson sign). Prolonged follow-up is mandatory for early detection of possible malignant changes.

Matched MeSH terms: Dermoscopy
Factors inducing in-stent restenosis: an in-vitro model

Santin M, Morris C, Harrison M, Mikhalovska L, Lloyd AW, Mikhalovsky S

Med J Malaysia, 2004 May;59 Suppl B:93-4.
PMID: 15468834

In-stent restenosis is caused by the proliferation of the smooth muscle cells (SMCs) following a host response towards the implanted device. However, the precise biochemical and cellular mechanisms are still not completely understood. In this paper, the behaviour of SMCs has been investigated by an in vitro model where the cells were stimulated by platelet derived growth factor (PDGF) on tissue-like substrates as well as on biomaterials such as stainless steel (St) and diamond-like carbon (DLC)-coated St. The results demonstrated that SMCs have a completely different adhesion mode on St and become particularly prone to proliferation and pro-inflammatory cytokine secretion under PDGF stimulus. This would suggest that restenosis may caused by the accidental contact of the SMC with the St substrate under an inflammatory insult.

Matched MeSH terms: Dermoscopy

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