Using adaptive thresholding and skewness correction to detect gray areas in melanoma \u3ci\u3ein situ\u3c/i\u3e images

The incidence of melanoma in situ (MIS) is growing significantly. Detection at the MIS stage provides the highest cure rate for melanoma, but reliable detection of MIS with dermoscopy alone is not yet possible. Adjunct dermoscopic instrumentation using digital image analysis may allow more accurate detection of MIS. Gray areas are a critical component of MIS diagnosis, but automatic detection of these areas remains difficult because similar gray areas are also found in benign lesions. This paper proposes a novel adaptive thresholding technique for automatically detecting gray areas specific to MIS. The proposed model uses only MIS dermoscopic images to precisely determine gray area characteristics specific to MIS. To this aim, statistical histogram analysis is employed in multiple color spaces. It is demonstrated that skew deviation due to an asymmetric histogram distorts the color detection process. We introduce a skew estimation technique that enables histogram asymmetry correction facilitating improved adaptive thresholding results. These histogram statistical methods may be extended to detect any local image area defined by histograms --Abstract, page iv

Skin Lesion Segmentation in Dermoscopic Images with Noisy Data

We Propose a Deep Learning Approach to Segment the Skin Lesion in Dermoscopic Images. the Proposed Network Architecture Uses a Pretrained Efficient Net Model in the Encoder and Squeeze-And-Excitation Residual Structures in the Decoder. We Applied This Approach on the Publicly Available International Skin Imaging Collaboration (ISIC) 2017 Challenge Skin Lesion Segmentation Dataset. This Benchmark Dataset Has Been Widely Used in Previous Studies. We Observed Many Inaccurate or Noisy Ground Truth Labels. to Reduce Noisy Data, We Manually Sorted All Ground Truth Labels into Three Categories — Good, Mildly Noisy, and Noisy Labels. Furthermore, We Investigated the Effect of Such Noisy Labels in Training and Test Sets. Our Test Results Show that the Proposed Method Achieved Jaccard Scores of 0.807 on the Official ISIC 2017 Test Set and 0.832 on the Curated ISIC 2017 Test Set, Exhibiting Better Performance Than Previously Reported Methods. Furthermore, the Experimental Results Showed that the Noisy Labels in the Training Set Did Not Lower the Segmentation Performance. However, the Noisy Labels in the Test Set Adversely Affected the Evaluation Scores. We Recommend that the Noisy Labels Should Be Avoided in the Test Set in Future Studies for Accurate Evaluation of the Segmentation Algorithms

SharpRazor: Automatic Removal Of Hair And Ruler Marks From Dermoscopy Images

Background: The removal of hair and ruler marks is critical in handcrafted image analysis of dermoscopic skin lesions. No other dermoscopic artifacts cause more problems in segmentation and structure detection. Purpose: The aim of the work is to detect both white and black hair, artifacts and finally inpaint correctly the image. Method: We introduce a new algorithm: SharpRazor, to detect hair and ruler marks and remove them from the image. Our multiple-filter approach detects hairs of varying widths within varying backgrounds, while avoiding detection of vessels and bubbles. The proposed algorithm utilizes grayscale plane modification, hair enhancement, segmentation using tri-directional gradients, and multiple filters for hair of varying widths. We develop an alternate entropy-based processing adaptive thresholding method. White or light-colored hair, and ruler marks are detected separately and added to the final hair mask. A classifier removes noise objects. Finally, a new technique of inpainting is presented, and this is utilized to remove the detected object from the lesion image. Results: The proposed algorithm is tested on two datasets, and compares with seven existing methods measuring accuracy, precision, recall, dice, and Jaccard scores. SharpRazor is shown to outperform existing methods. Conclusion: The Shaprazor techniques show the promise to reach the purpose of removing and inpaint both dark and white hair in a wide variety of lesions

Improving Automatic Melanoma Diagnosis using Deep Learning-Based Segmentation of Irregular Networks

Deep Learning Has Achieved Significant Success in Malignant Melanoma Diagnosis. These Diagnostic Models Are Undergoing a Transition into Clinical Use. However, with Melanoma Diagnostic Accuracy in the Range of Ninety Percent, a Significant Minority of Melanomas Are Missed by Deep Learning. Many of the Melanomas Missed Have Irregular Pigment Networks Visible using Dermoscopy. This Research Presents an Annotated Irregular Network Database and Develops a Classification Pipeline that Fuses Deep Learning Image-Level Results with Conventional Hand-Crafted Features from Irregular Pigment Networks. We Identified and Annotated 487 Unique Dermoscopic Melanoma Lesions from Images in the ISIC 2019 Dermoscopic Dataset to Create a Ground-Truth Irregular Pigment Network Dataset. We Trained Multiple Transfer Learned Segmentation Models to Detect Irregular Networks in This Training Set. a Separate, Mutually Exclusive Subset of the International Skin Imaging Collaboration (ISIC) 2019 Dataset with 500 Melanomas and 500 Benign Lesions Was Used for Training and Testing Deep Learning Models for the Binary Classification of Melanoma Versus Benign. the Best Segmentation Model, U-Net++, Generated Irregular Network Masks on the 1000-Image Dataset. Other Classical Color, Texture, and Shape Features Were Calculated for the Irregular Network Areas. We Achieved an Increase in the Recall of Melanoma Versus Benign of 11% and in Accuracy of 2% over DL-Only Models using Conventional Classifiers in a Sequential Pipeline based on the Cascade Generalization Framework, with the Highest Increase in Recall Accompanying the Use of the Random Forest Algorithm. the Proposed Approach Facilitates Leveraging the Strengths of Both Deep Learning and Conventional Image Processing Techniques to Improve the Accuracy of Melanoma Diagnosis. Further Research Combining Deep Learning with Conventional Image Processing on Automatically Detected Dermoscopic Features is Warranted

Chimeranet: U-Net for Hair Detection in Dermoscopic Skin Lesion Images

Hair and ruler mark structures in dermoscopic images are an obstacle preventing accurate image segmentation and detection of critical network features. Recognition and removal of hairs from images can be challenging, especially for hairs that are thin, overlapping, faded, or of similar color as skin or overlaid on a textured lesion. This paper proposes a novel deep learning (DL) technique to detect hair and ruler marks in skin lesion images. Our proposed ChimeraNet is an encoder-decoder architecture that employs pretrained EfficientNet in the encoder and squeeze-and-excitation residual (SERes) structures in the decoder. We applied this approach at multiple image sizes and evaluated it using the publicly available HAM10000 (ISIC2018 Task 3) skin lesion dataset. Our test results show that the largest image size (448 x 448) gave the highest accuracy of 98.23 and Jaccard index of 0.65 on the HAM10000 (ISIC 2018 Task 3) skin lesion dataset, exhibiting better performance than for two well-known deep learning approaches, U-Net and ResUNet-a. We found the Dice loss function to give the best results for all measures. Further evaluated on 25 additional test images, the technique yields state-of-the-art accuracy compared to 8 previously reported classical techniques. We conclude that the proposed ChimeraNet architecture may enable improved detection of fine image structures. Further application of DL techniques to detect dermoscopy structures is warranted

Medical Image Processing in the Age of Deep Learning -- Is There Still Room for Conventional Medical Image Processing Techniques?

Deep learning, in particular convolutional neural networks, has increasingly been applied to medical images. Advances in hardware coupled with availability of increasingly large data sets have fueled this rise. Results have shattered expectations. But it would be premature to cast aside conventional machine learning and image processing techniques. All that deep learning comes at a cost, the need for very large datasets. We discuss the role of conventional manually tuned features combined with deep learning. This process of fusing conventional image processing techniques with deep learning can yield results that are superior to those obtained by either learning method in isolation. In this article, we review the rise of deep learning in medical image processing and the recent onset of fusion of learning methods. We discuss supervision equilibrium point and the factors that favor the role of fusion methods for histopathology and quasihistopathology modalities

Real-World Pill Segmentation Based on Superpixel Merge using Region Adjacency Graph

Misidentified or unidentified prescription pills are an increasing challenge for all caregivers, both families and professionals. Errors in pill identification may lead to serious or fatal adverse events. To respond to this challenge, a fast and reliable automated pill identification technique is needed. The first and most critical step in pill identification is segmentation of the pill from the background. The goals of segmentation are to eliminate both false detection of background area and false omission of pill area. Introduction of either type of error can cause errors in color or shape analysis and can lead to pill misidentification. The real-world consumer images used in this research provide significant segmentation challenges due to varied backgrounds and lighting conditions. This paper proposes a color image segmentation algorithm by generating superpixels using the Simple Linear Iterative Clustering (SLIC) algorithm and merging the superpixels by thresholding the region adjacency graphs. Post-processing steps are given to result in accurate pill segmentation. The segmentation accuracy is evaluated by comparing the consumer-quality pill image segmentation masks to the high quality reference pill image masks

Optical Oxygen Sensor Patch Printed with Polystyrene Microparticles-Based Ink on Flexible Substrate

Optical oxygen sensors based on photoluminescence quenching have gained increasing attention as a superior method for continuous monitoring of oxygen in a growing number of applications. A simple and low-cost fabrication technique was developed to produce sensor arrays capable of two-dimensional oxygen tension measurement. Sensor patches were printed on polyvinylidene chloride film using an oxygen-sensitive ink cocktail, prepared by immobilizing Pt (II) meso-tetra(pentafluorophenyl)porphine (PtTFPP) in monodispersed polystyrene microparticles. The dispersion media of the ink cocktail, high molecular weight polyvinyl pyrrolidone suspended in 50% ethanol (v/v in water), allowed adhesion promotion and compatibility with most common polymeric substrates. Ink phosphorescence intensity was found to vary primarily with fluorophore concentration and to a lesser extent with polystyrene particle size. The sensor performance was investigated as a function of oxygen concentrations employing two different techniques: a multi-frequency phase fluorometer and smart phone-based image acquisition. The printed sensor patch showed fast and repetitive response over 0-21% oxygen concentrations with high linearity (with R2 \u3e 0.99) in a Stern-Volmer plot, and sensitivity of I0/I21 \u3e 1.55. The optical sensor response on a surface was investigated further using two-dimensional images which were captured and analyzed under different oxygen environment. Printed sensor patch along with imaging read-out technique make an ideal platform for early detection of surface wounds associated with tissue oxygen

Adaptive Segmentation of Gray Areas in Dermoscopy Images

In this work, a dermoscopic image analysis technique is proposed. A novel approach, based on the detection of gray areas using image analysis techniques is explored. To this aim, a statistical histogram analysis is carried out using the HSB color space to derive the relationship between the skewness and the mean of the brightness color plane histogram. The derived framework is used for adaptive thresholding of gray area regions within a skin lesion image

Dangerous Drug Reactions on the S&A iPhone App [abstract]

Computational Infrastructure and Informatics Poster SessionThe S&A drug database for handhelds, sold to dermatologists for 8 years, was uploaded as a new iPhone application (app), February 2010. Our program notes all warnings at the several FDA levels: boxed warnings, bold warnings, warnings, and adverse effects, reflecting greater interest in drug warnings by all parties, including consumers, regulators, industry. The FDA has a program called MedWatch, which collects reports of adverse drug reactions, including serious and fatal reactions. In 2007, The Institute for Safe Medical Practices (ISMP) published a list of the fifteen drugs most often reported as primary suspects for serious and fatal reactions in the FDA's MedWatch program for the eight years ending in December, 2005. Our iPhone app now notes all drugs which made this list. Using knowledge gained working with the ISMP during summer, 2009, S&A researchers, including 2 S&T seniors, are working with MedWatch data to revise and update the old fatal drug list. The new information will be placed on the S&A website and the iPhone app during Spring, 2010. The quarterly data will allow consumers to see how the fatality rates for different drugs are changing. In addition, the changes will allow S&A to look for a signal for specific reactions. The proportional reporting ratio (PRR) was used to search the MedWatch data from 2004-2008 and discover that ciprofloxacin was the leading primary suspect drug in deaths in elderly patients from severe skin reactions during the period 2004-2008