Accurate segmentation and registration of skin lesion images to evaluate lesion change

Skin cancer is a major health problem. There are several techniques to help diagnose skin lesions from a captured image. Computer-aided diagnosis (CAD) systems operate on single images of skin lesions, extracting lesion features to further classify them and help the specialists. Accurate feature extraction, which later on depends on precise lesion segmentation, is key for the performance of these systems. In this paper, we present a skin lesion segmentation algorithm based on a novel adaptation of superpixels techniques and achieve the best reported results for the ISIC 2017 challenge dataset. Additionally, CAD systems have paid little attention to a critical criterion in skin lesion diagnosis: the lesion's evolution. This requires operating on two or more images of the same lesion, captured at different times but with a comparable scale, orientation, and point of view; in other words, an image registration process should first be performed. We also propose in this work, an image registration approach that outperforms top image registration techniques. Combined with the proposed lesion segmentation algorithm, this allows for the accurate extraction of features to assess the evolution of the lesion. We present a case study with the lesion-size feature, paving the way for the development of automatic systems to easily evaluate skin lesion evolutionThis work was supported in part by the Spanish Government (HAVideo, TEC2014-53176-R) and in part by the TEC department (Universidad Autonoma de Madrid

Smoothing of ultrasound images using a new selective average filter

Ultrasound images are strongly affected by speckle noise making visual and computational analysis of the structures more difficult. Usually, the interference caused by this kind of noise reduces the efficiency of extraction and interpretation of the structural features of interest. In order to overcome this problem, a new method of selective smoothing based on average filtering and the radiation intensity of the image pixels is proposed. The main idea of this new method is to identify the pixels belonging to the borders of the structures of interest in the image, and then apply a reduced smoothing to these pixels, whilst applying more intense smoothing to the remaining pixels. Experimental tests were conducted using synthetic ultrasound images with speckle noisy added and real ultrasound images from the female pelvic cavity. The new smoothing method is able to perform selective smoothing in the input images, enhancing the transitions between the different structures presented. The results achieved are promising, as the evaluation analysis performed shows that the developed method is more efficient in removing speckle noise from the ultrasound images compared to other current methods. This improvement is because it is able to adapt the filtering process according to the image contents, thus avoiding the loss of any relevant structural features in the input images

Automated image analysis systems to quantify physical and behavioral attributes of biological entities

All life forms in nature have physical and behavioral attributes which help them survive and thrive in their environment. Technologies, both within the areas of hardware systems and data processing algorithms, have been developed to extract relevant information about these attributes. Understanding the complex interplay of physical and behavioral attributes is proving important towards identifying the phenotypic traits displayed by organisms. This thesis attempts to leverage the unique advantages of portable/mobile hardware systems and data processing algorithms for applications in three areas of bioengineering: skin cancer diagnostics, plant parasitic nematology, and neglected tropical disease. Chapter 1 discusses the challenges in developing image processing systems that meet the requirements of low cost, portability, high-throughput, and accuracy. The research motivation is inspired by these challenges within the areas of bioengineering that are still elusive to the technological advancements in hardware electronics and data processing algorithms. A literature review is provided on existing image analysis systems that highlight the limitations of current methods and provide scope for improvement. Chapter 2 is related to the area of skin cancer diagnostics where a novel smartphone-based method is presented for the early detection of melanoma in the comfort of a home setting. A smartphone application is developed along with imaging accessories to capture images of skin lesions and classify them as benign or cancerous. Information is extracted about the physical attributes of a skin lesion such as asymmetry, border irregularity, number of colors, and diameter. Machine learning is employed to train the smartphone application using both dermoscopic and digital lesion images. Chapter 3 is related to the area of plant parasitic nematology where automated methods are presented to provide the nematode egg count from soil samples. A new lensless imaging system is built to record holographic videos of soil particles flowing through microscale flow assays. Software algorithms are written to automatically identify the nematode eggs from low resolution holographic videos or images captured from a scanner. Deep learning algorithm was incorporated to improve the learning process and train the software model. Chapter 4 is related to the area of neglected tropical diseases where new worm tracking systems have been developed to characterize the phenotypic traits of Brugia malayi adult male worms and their microfilaria. The worm tracking algorithm recognizes behavioral attributes of these parasites by extracting a number of features related to their movement and body posture. An imaging platform is optimized to capture high-resolution videos with appropriate field of view of B. malayi. The relevance of each behavioral feature was evaluated through drug screening using three common antifilarial compounds. The abovementioned image analysis systems provide unique advantages to the current experimental methods. For example, the smartphone-based software application is a low-cost alternative to skin cancer diagnostics compared to standard dermoscopy available in skin clinics. The lensless imaging system is a low-cost and high-throughput alternative for obtaining egg count densities of plant parasitic nematodes compared with visual counting under a microscope by trained personnel. The B. malayi worm tracking system provides an alternative to available C. elegans tracking software with options to extract multiple parameters related to its body skeleton and posture

A computational approach for detecting pigmented skin lesions in macroscopic images

Skin cancer is considered one of the most common types of cancer in several countries and its incidencerate has increased in recent years. Computational methods have been developed to assist dermatologistsin early diagnosis of skin cancer. Computational analysis of skin lesion images has become a challengingresearch area due to the difficulty in discerning some types of skin lesions. A novel computational approachis presented for extracting skin lesion features from images based on asymmetry, border, colourand texture analysis, in order to diagnose skin lesion types. The approach is based on an anisotropic diffusionfilter, an active contour model without edges and a support vector machine. Experiments wereperformed regarding the segmentation and classification of pigmented skin lesions in macroscopic images,with the results obtained being very promising

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

Automated classification of malignant melanoma based on detection of atypical pigment network in dermoscopy images of skin lesions

“Melanoma causes more deaths than any other form of skin cancer. Early melanoma detection is important to prevent progression to a more deadly stage. Automated computer-based identification of melanoma from dermoscopic images of skin lesions is the most efficient method in early diagnosis. An automated melanoma identification system must include multiple steps, involving lesion segmentation, feature extraction, feature combination and classification. In this research, a classifier-based approach for automatically selecting a lesion border mask for segmentation of dermoscopic skin lesion images is presented. A logistic regression based model selects a single lesion border mask from multiple border masks generated by multiple lesion segmentation algorithms. This research also presents a method of segmenting atypical pigment network (APN) based on variance in the red plane in the lesion area of a dermoscopic image. Features extracted from APN regions are used in automated classification of melanoma. The automated identification of melanoma is further improved by fusion of other features relevant to melanoma detection. This research uses clinical features, APN features, median split cluster features, pink area features, white area features and salient point features in various hierarchical combinations to improve the overall performance in melanoma identification. A training set of 837 dermoscopic skin lesion images together with a disjoint test set of 804 dermoscopic skin lesion images are used in this research to produce the experimental findings”--Abstract, page iv

Computational Methods for Pigmented Skin Lesion Classification in Images: Review and Future Trends

Skin cancer is considered as one of the most common types of cancer in several countries, and its incidence rate has increased in recent years. Melanoma cases have caused an increasing number of deaths worldwide, since this type of skin cancer is the most aggressive compared to other types. Computational methods have been developed to assist dermatologists in early diagnosis of skin cancer. An overview of the main and current computational methods that have been proposed for pattern analysis and pigmented skin lesion classification is addressed in this review. In addition, a discussion about the application of such methods, as well as future trends, is also provided. Several methods for feature extraction from both macroscopic and dermoscopic images and models for feature selection are introduced and discussed. Furthermore, classification algorithms and evaluation procedures are described, and performance results for lesion classification and pattern analysis are given

(SEMI)-AUTOMATED ANALYSIS OF MELANOCYTIC LESIONS

Melanoma is a very aggressive form of skin cancer whose incidence has constantly grown in the last 50 years. To increase the survival rate, an early diagnosis followed by a prompt excision is crucial and requires an accurate and periodic analysis of the patient's melanocytic lesions. We have developed an hardware and software solution named Mole Mapper to assist the dermatologists during the diagnostic process. The goal is to increase the accuracy of the diagnosis, accelerating the entire process at the same time. This is achieved through an automated analysis of the dermatoscopic images which computes and highlights the proper information to the dermatologist. In this thesis we present the 3 main algorithms that have been implemented into the Mole Mapper: A robust segmentation of the melanocytic lesion, which is the starting point for any other image processing algorithm and which allows the extraction of useful information about the lesion's shape and size. It outperforms the speed and quality of other state-of-the-art methods, with a precision that meets a Senior Dermatologist's standard and an execution time that allows for real-time video processing; A virtual shaving algorithm, which increases the precision and robustness of the other computer vision algorithms and provides the dermatologist with a hair-free image to be used during the evaluation process. It matches the quality of state-of-the-art methods but requires only a fraction of the computational time, allowing for computation on a mobile device in a time-frame compatible with an interactive GUI; A registration algorithm through which to study the evolution of the lesion over time, highlighting any unexpected anomalies and variations. Since a standard approach to this problem has not yet been proposed, we define the scope and constraints of the problem; we analyze the results and issues of standard registration techniques; and finally, we propose an algorithm with a speed compatible with Mole Mapper's constraints and with an accuracy comparable to the registration performed by a human operator