A perceptually oriented method for contrast enhancement and segmentation of dermoscopy images

All you need is data preparation: A systematic review of image harmonization techniques in Multi-center/device studies for medical support systems

BACKGROUND AND OBJECTIVES

Artificial intelligence (AI) models trained on multi-centric and multi-device studies can provide more robust insights and research findings compared to single-center studies. However, variability in acquisition protocols and equipment can introduce inconsistencies that hamper the effective pooling of multi-source datasets. This systematic review evaluates strategies for image harmonization, which standardizes appearances to enable reliable AI analysis of multi-source medical imaging.

METHODS

A literature search using PRISMA guidelines was conducted to identify relevant papers published between 2013 and 2023 analyzing multi-centric and multi-device medical imaging studies that utilized image harmonization approaches.

RESULTS

Common image harmonization techniques included grayscale normalization (improving classification accuracy by up to 24.42 %), resampling (increasing the percentage of robust radiomics features from 59.5 % to 89.25 %), and color normalization (enhancing AUC by up to 0.25 in external test sets). Initially, mathematical and statistical methods dominated, but machine and deep learning adoption has risen recently. Color imaging modalities like digital pathology and dermatology have remained prominent application areas, though harmonization efforts have expanded to diverse fields including radiology, nuclear medicine, and ultrasound imaging. In all the modalities covered by this review, image harmonization improved AI performance, with increasing of up to 24.42 % in classification accuracy and 47 % in segmentation Dice scores.

CONCLUSIONS

Continued progress in image harmonization represents a promising strategy for advancing healthcare by enabling large-scale, reliable analysis of integrated multi-source datasets using AI. Standardizing imaging data across clinical settings can help realize personalized, evidence-based care supported by data-driven technologies while mitigating biases associated with specific populations or acquisition protocols.

Skin Lesion Segmentation by U-Net with Adaptive Skip Connection and Structural Awareness

Skin lesion segmentation is one of the pivotal stages in the diagnosis of melanoma. Many methods have been proposed but, to date, this is still a challenging task. Variations in size and color, the fuzzy boundary and the low contrast between lesion and normal skin are the adverse factors for deficient or excessive delineation of lesions, or even inaccurate lesion location detection. In this paper, to counter these problems, we introduce a deep learning method based on U-Net architecture, which performs three tasks, namely lesion segmentation, boundary distance map regression and contour detection. The two auxiliary tasks provide an awareness of boundary and shape to the main encoder, which improves the object localization and pixel-wise classification in the transition region from lesion tissues to healthy tissues. Moreover, concerning the large variation in size, the Selective Kernel modules, which are placed in the skip connections, transfer the multi-receptive field features from the encoder to the decoder. Our methods are evaluated on three publicly available datasets: ISBI2016, ISBI 2017 and PH2. The extensive experimental results show the effectiveness of the proposed method in the task of skin lesion segmentation.

A multilevel features selection framework for skin lesion classification

Melanoma is considered to be one of the deadliest skin cancer types, whose occurring frequency elevated in the last few years; its earlier diagnosis, however, significantly increases the chances of patients’ survival. In the quest for the same, a few computer based methods, capable of diagnosing the skin lesion at initial stages, have been recently proposed. Despite some success, however, margin exists, due to which the machine learning community still considers this an outstanding research challenge. In this work, we come up with a novel framework for skin lesion classification, which integrates deep features information to generate most discriminant feature vector, with an advantage of preserving the original feature space. We utilize recent deep models for feature extraction, and by taking advantage of transfer learning. Initially, the dermoscopic images are segmented, and the lesion region is extracted, which is later subjected to retrain the selected deep models to generate fused feature vectors. In the second phase, a framework for most discriminant feature selection and dimensionality reduction is proposed, entropy-controlled neighborhood component analysis (ECNCA). This hierarchical framework optimizes fused features by selecting the principle components and extricating the redundant and irrelevant data. The effectiveness of our design is validated on four benchmark dermoscopic datasets; PH2, ISIC MSK, ISIC UDA, and ISBI-2017. To authenticate the proposed method, a fair comparison with the existing techniques is also provided. The simulation results clearly show that the proposed design is accurate enough to categorize the skin lesion with 98.8%, 99.2% and 97.1% and 95.9% accuracy with the selected classifiers on all four datasets, and by utilizing less than 3% features.

Melanoma Detection in Dermoscopic Images using Color Features

Color is considered to be a major characteristic feature that is used for distinguishing benign and malignant melanocytic lesions. Most of malignant melanomas are characterized by the presence of six suspicious colors inspired from the ABCD dermoscopic rule. The presence of these suspicious colors histopathologically indicates the presence of melanin in the deeper layers of the epidermis and dermis. The objective of the proposed work is to evaluate the role of color features, a set of fifteen color features have been extracted from the region of interest to determine the role of color in malignancy detection. Further, a set of ensemble classifiers with dynamic selection techniques are used for classification of the extracted features, yielding an average accuracy of 87.5% for classifying benign and malignant lesions.

Computer-assisted diagnosis techniques (dermoscopy and spectroscopy-based) for diagnosing skin cancer in adults

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.

Computer Based Melanocytic and Nevus Image Enhancement and Segmentation

Digital dermoscopy aids dermatologists in monitoring potentially cancerous skin lesions. Melanoma is the 5th common form of skin cancer that is rare but the most dangerous. Melanoma is curable if it is detected at an early stage. Automated segmentation of cancerous lesion from normal skin is the most critical yet tricky part in computerized lesion detection and classification. The effectiveness and accuracy of lesion classification are critically dependent on the quality of lesion segmentation. In this paper, we have proposed a novel approach that can automatically preprocess the image and then segment the lesion. The system filters unwanted artifacts including hairs, gel, bubbles, and specular reflection. A novel approach is presented using the concept of wavelets for detection and inpainting the hairs present in the cancer images. The contrast of lesion with the skin is enhanced using adaptive sigmoidal function that takes care of the localized intensity distribution within a given lesion's images. We then present a segmentation approach to precisely segment the lesion from the background. The proposed approach is tested on the European database of dermoscopic images. Results are compared with the competitors to demonstrate the superiority of the suggested approach.

Computational methods for the image segmentation of pigmented skin lesions: A review

BACKGROUND AND OBJECTIVES

Because skin cancer affects millions of people worldwide, computational methods for the segmentation of pigmented skin lesions in images have been developed in order to assist dermatologists in their diagnosis. This paper aims to present a review of the current methods, and outline a comparative analysis with regards to several of the fundamental steps of image processing, such as image acquisition, pre-processing and segmentation.

METHODS

Techniques that have been proposed to achieve these tasks were identified and reviewed. As to the image segmentation task, the techniques were classified according to their principle.

RESULTS

The techniques employed in each step are explained, and their strengths and weaknesses are identified. In addition, several of the reviewed techniques are applied to macroscopic and dermoscopy images in order to exemplify their results.

CONCLUSIONS

The image segmentation of skin lesions has been addressed successfully in many studies; however, there is a demand for new methodologies in order to improve the efficiency.

The feasibility of using manual segmentation in a multifeature computer-aided diagnosis system for classification of skin lesions: a retrospective comparative study

OBJECTIVES

To investigate the feasibility of manual segmentation by users of different backgrounds in a previously developed multifeature computer-aided diagnosis (CADx) system to classify melanocytic and non-melanocytic skin lesions based on conventional digital photographic images.

METHODS

In total, 347 conventional photographs of melanocytic and non-melanocytic skin lesions were retrospectively reviewed, and manually segmented by two groups of physicians, dermatologists and general practitioners, as well as by an automated segmentation software program, JSEG. The performance of CADx based on inputs from these two groups of physicians and that of the JSEG program was compared using feature agreement analysis.

RESULTS

The estimated area under the receiver operating characteristic curve for classification of benign or malignant skin lesions based were comparable on individual segmentation by the gold standard (0.893, 95% CI 0.856 to 0.930), dermatologists (0.886, 95% CI 0.863 to 0.908), general practitioners (0.883, 95% CI 0.864 to 0.903) and JSEG (0.856, 95% CI 0.812 to 0.899). The agreement in the malignancy probability scores among the physicians was excellent (intraclass correlation coefficient: 0.91). By selecting an optimal cut-off value of malignancy probability score, the sensitivity and specificity were 80.07% and 81.47% for dermatologists and 79.90% and 80.20% for general practitioners.

CONCLUSIONS

This study suggests that manual segmentation by general practitioners is feasible in the described CADx system for classifying benign and malignant skin lesions.

Colour contrasting between tissues predicts the resection in 5-aminolevulinic acid-guided surgery of malignant gliomas

Due to the various intensities of 5-aminolevulinic acid (5-ALA) fluorescence, neurosurgeons tend to be uncertain about which tissues to resect. This study aimed to reveal the shortcomings of the human visual perception of fluorescence, particularly the factors guiding the tissue removal and the correlation of fluorescence with contrast enhancement (CE) on magnetic resonance imaging (MRI). Various colour features [CIE L*a*b* colour space, colour difference described by ΔE and contrast ratio (CR)] of total 206 noticed fluorescent areas and their surroundings were measured from the video recordings of 21 primary high grade glioma (HGG) surgeries. The position of a fluorescent region was related to the corecorded navigational image. Following early postoperative MRI, 17 additional regions of corresponding to CE remnants were identified, their colour features were compared to the resected CEs. The targeted video post-processing method was designed, based on the results. There were no complications attributed to 5-ALA use and the median survival was <10 months. 82.5 % of recognised fluorescent areas were removed. Colour spaces of the resected regions and their backgrounds did not overlap. Opposite to the separate colour components (p > 0.05), the distant background colour (p < 0.05) and higher CR and ΔE (p < 0.01) determined the resection of a fluorescent region. Noneloquent location and CR both independently increased the resection rate in logistic regression. However, greater area under the receiver operating characteristic curve (AUC) in case of CR (AUC = 0.78; 95 % CI 0.71-0.83) determined its dominant role in neurosurgeon's fluorescence perception. CE regions presented with a significantly more saturated shade of violet (consistently higher a* and b*) than other tumour parts (p < 0.05). Regions corresponding to tumour remnants had a significantly lower a* component value (p = 0.02) as well as a lower ΔE than the matched background (AUC = 0.73; 95 % CI 0.65-0.80). In order to increase the resection rate, ΔE > 60 was needed. These results directed essential improvements in the 5-ALA fluorescence visualisation toward enhanced resection rate. The conventional filtering, unadjusted to the 5-ALA colour space converted some background shades to colours resembling relevant fluorescence. This is one of the first studies to demonstrate that perceived colours, their contrasting and CR are of significance in the decision-making during HGG 5-ALA fluorescence-guided surgery. Irrespective of the shortcomings of conventional video filtering, further development of a tailored post-processed contrast stretching will allow to achieve safe and radical tumour resection.

Unified approach for lesion border detection based on mixture modeling and local entropy thresholding

BACKGROUND/PURPOSE

Computer-aided design (CAD) methods are highly valuable for the analysis of skin lesions using digital dermoscopy due to low rate of diagnostic accuracy of expert dermatologist. In computerized diagnostic methods, automatic border detection is the first and crucial step.

METHOD

In this study, a novel unified approach is proposed for automatic border detection (ABD). A preprocessing step is performed by normalized smoothing filter (NSF) to reduce background noise. Mixture models technique is then utilized to initially segment the lesion area roughly. Afterward, local entropy thresholding is performed to extract the lesion candidate pixels and the lesion border is smoothed using morphological reconstruction.

RESULTS

The overall ABD system is tested on a set of 100 dermoscopy images with ground truth. A comparative study was conducted with the other three state-of-the-art methods using statistical metrics. This ABD technique has the minimal average error probability rate of 5%, true detection of 92.10% and false positive rate of 6.41%.

CONCLUSION

Results demonstrate that the proposed method segments the lesion area accurately. Sample dataset and execute software are available online and can be downloaded from: http://cs.ntu.edu.pk/research.