Image analysis algorithms for feature extraction in eye fundus images

Retinal images are widely used for diagnostic purposes by ophthalmolo- gists. Therefore, these images are suitable for digital image analysis for their visual enhancement and pathological risk or damage detection. Here, we implement a lu- minosity and contrast enhancement technique based on domain knowledge. We also review and analyze a previous approach in optic nerve head segmentation to extend its applicability to non circular shaped contours. We introduce a di erent strategy based on the use of active contours

State-of-the-art active optical techniques for three-dimensional surface metrology: a review [Invited]

This paper reviews recent developments of non-contact three-dimensional (3D) surface metrology using an active structured optical probe. We focus primarily on those active non-contact 3D surface measurement techniques that could be applicable to the manufacturing industry. We discuss principles of each technology, and its advantageous characteristics as well as limitations. Towards the end, we discuss our perspectives on the current technological challenges in designing and implementing these methods in practical applications.Purdue Universit

Comprehensive retinal image analysis: image processing and feature extraction techniques oriented to the clinical task

Medical digital imaging has become a key element of modern health care procedures. It provides a visual documentation, a permanent record for the patients, and most importantly the ability to extract information about many diseases. Ophthalmology is a field that is heavily dependent on the analysis of digital images because they can aid in establishing an early diagnosis even before the first symptoms appear. This dissertation contributes to the digital analysis of such images and the problems that arise along the imaging pipeline, a field that is commonly referred to as retinal image analysis. We have dealt with and proposed solutions to problems that arise in retinal image acquisition and longitudinal monitoring of retinal disease evolution. Specifically, non-uniform illumination, poor image quality, automated focusing, and multichannel analysis. However, there are many unavoidable situations in which images of poor quality, like blurred retinal images because of aberrations in the eye, are acquired. To address this problem we have proposed two approaches for blind deconvolution of blurred retinal images. In the first approach, we consider the blur to be space-invariant and later in the second approach we extend the work and propose a more general space-variant scheme. For the development of the algorithms we have built preprocessing solutions that have enabled the extraction of retinal features of medical relevancy, like the segmentation of the optic disc and the detection and visualization of longitudinal structural changes in the retina. Encouraging experimental results carried out on real retinal images coming from the clinical setting demonstrate the applicability of our proposed solutions

Skin prick test wheal detection in 3D images via convolutional neural networks

The skin prick test (SPT) is performed to diagnose different types of allergies. This medical procedure requires measuring the size of the skin wheals that appear when the test is performed. However, the manual measurement method is cumbersome and suffers from intraand inter-observer errors. Thus, multiple approaches have been developed to improve the reproducibility of the test. This work aims to improve part of the automated reading of the SPT to improve the reliability of the wheal detection procedure through the use of convolutional neural networks (CNN). Our proposal starts from the 3D images of the SPT from the arm of patients. They are processed for global surface removal, and then a CNN is trained to produce an output mask that detects the wheals. Finally, the contour of each wheal and its largest diameter is obtained. Encouraging results with mean difference 0.966 mm and mean coefficient of variation 7.29% show that the proposed method provides reliable automated skin wheal detection

Method for large-scale structured-light system calibration

We propose a multi-stage calibration method for increasing the overall accuracy of a large-scale structured light system by leveraging the conventional stereo calibration approach using a pinhole model. We first calibrate the intrinsic parameters at a near distance and then the extrinsic parameters with a low-cost large-calibration target at the designed measurement distance. Finally, we estimate pixel-wise errors from standard stereo 3D reconstructions and determine the pixel-wise phase-to-coordinate relationships using low-order polynomials. The calibrated pixel-wise polynomial functions can be used for 3D reconstruction for a given pixel phase value. We experimentally demonstrated that our proposed method achieves high accuracy for a large volume: sub-millimeter within 1200(H) × 800 (V) × 1000(D) mm3

Multi-target Attachment for Surgical Instrument Tracking

The pose estimation of a surgical instrument is a common problem in the new needs of medical science. Many instrument tracking methods use markers with a known geometry that allows for solving the instrument pose as detected by a camera. However, marker occlusion happens, and it hinders correct pose estimation. In this work, we propose an adaptable multi-target attachment with ArUco markers to solve occlusion problems on tracking a medical instrument like an ultrasound probe or a scalpel. Our multi-target system allows for precise and redundant real-time pose estimation implemented in OpenCV. Encouraging results show that the multi-target device may prove useful in the clinical settin

Three-dimensional multimodal medical imaging system based on freehand ultrasound and structured light

We propose a three-dimensional (3D) multimodal medical imaging system that combines freehand ultrasound and structured light 3D reconstruction in a single coordinate system without requiring registration. To the best of our knowledge, these techniques have not been combined as a multimodal imaging technique. The system complements the internal 3D information acquired with ultrasound with the external surface measured with the structured light technique. Moreover, the ultrasound probe’s optical tracking for pose estimation was implemented based on a convolutional neural network. Experimental results show the system’s high accuracy and reproducibility, as well as its potential for preoperative and intraoperative applications. The experimental multimodal error, or the distance from two surfaces obtained with different modalities, was 0.12 m

Detection and removal of dust artifacts in retinal images via sparse-based inpainting

Dust particle artifacts are present in all imaging modalities but have more adverse consequences in medical images like retinal images. They could be mistaken as small lesions, such as microaneurysms. We propose a method for detecting and accurately segmenting dust artifacts in retinal images based on multi-scale template-matching on several input images and an iterative segmentation via an inpainting approach. The inpainting is done through dictionary learning and sparse-based representation. The artifact segmentation is refined by comparing the original image to the initial restoration. On average, 90% of the dust artifacts were detected in the test images, with state-of-theart restoration results. All detected artifacts were accurately segmented and removed. Even the most challenging artifacts located on top of blood vessels were removed. Thus, ensuring the continuity of the retinal structures. The proposed method successfully detects and removes dust artifacts in retinal images, which could be used to avoid false-positive lesion detections or as an image quality criterion. An implementation of the proposed algorithm can be accessed and executed through a Code Ocean compute capsul

Image analysis algorithms for feature extraction in eye fundus images

Retinal images are widely used for diagnostic purposes by ophthalmolo- gists. Therefore, these images are suitable for digital image analysis for their visual enhancement and pathological risk or damage detection. Here, we implement a lu- minosity and contrast enhancement technique based on domain knowledge. We also review and analyze a previous approach in optic nerve head segmentation to extend its applicability to non circular shaped contours. We introduce a di erent strategy based on the use of active contours

Image analysis algorithms for feature extraction in eye fundus images

Retinal images are widely used for diagnostic purposes by ophthalmolo- gists. Therefore, these images are suitable for digital image analysis for their visual enhancement and pathological risk or damage detection. Here, we implement a lu- minosity and contrast enhancement technique based on domain knowledge. We also review and analyze a previous approach in optic nerve head segmentation to extend its applicability to non circular shaped contours. We introduce a di erent strategy based on the use of active contours