Muhammad Salman Haleem

Automatic extraction of retinal features from colour retinal images for glaucoma diagnosis: A review

Glaucoma is a group of eye diseases that have common traits such as, high eye pressure, damage to the Optic Nerve Head and gradual vision loss. It affects peripheral vision and eventually leads to blindness if left untreated. The current common methods of pre-diagnosis of Glaucoma include measurement of Intra-Ocular Pressure (IOP) using Tonometer, Pachymetry, Gonioscopy; which are performed manually by the clinicians. These tests are usually followed by Optic Nerve Head (ONH) Appearance examination for the confirmed diagnosis of Glaucoma. The diagnoses require regular monitoring, which is costly and time consuming. The accuracy and reliability of diagnosis is limited by the domain knowledge of different ophthalmologists. Therefore automatic diagnosis of Glaucoma attracts a lot of attention. This paper surveys the state-of-the-art of automatic extraction of anatomical features from retinal images to assist early diagnosis of the Glaucoma. We have conducted critical evaluation of the existing automatic extraction methods based on features including Optic Cup to Disc Ratio (CDR), Retinal Nerve Fibre Layer (RNFL), Peripapillary Atrophy (PPA), Neuroretinal Rim Notching, Vasculature Shift, etc., which adds value on efficient feature extraction related to Glaucoma diagnosis.

Regional Image Features Model for Automatic Classification between Normal and Glaucoma in Fundus and Scanning Laser Ophthalmoscopy (SLO) Images

Glaucoma is one of the leading causes of blindness worldwide. There is no cure for glaucoma but detection at its earliest stage and subsequent treatment can aid patients to prevent blindness. Currently, optic disc and retinal imaging facilitates glaucoma detection but this method requires manual post-imaging modifications that are time-consuming and subjective to image assessment by human observers. Therefore, it is necessary to automate this process. In this work, we have first proposed a novel computer aided approach for automatic glaucoma detection based on Regional Image Features Model (RIFM) which can automatically perform classification between normal and glaucoma images on the basis of regional information. Different from all the existing methods, our approach can extract both geometric (e.g. morphometric properties) and non-geometric based properties (e.g. pixel appearance/intensity values, texture) from images and significantly increase the classification performance. Our proposed approach consists of three new major contributions including automatic localisation of optic disc, automatic segmentation of disc, and classification between normal and glaucoma based on geometric and non-geometric properties of different regions of an image. We have compared our method with existing approaches and tested it on both fundus and Scanning laser ophthalmoscopy (SLO) images. The experimental results show that our proposed approach outperforms the state-of-the-art approaches using either geometric or non-geometric properties. The overall glaucoma classification accuracy for fundus images is 94.4 % and accuracy of detection of suspicion of glaucoma in SLO images is 93.9 %.

Retinal Area Detector From Scanning Laser Ophthalmoscope (SLO) Images for Diagnosing Retinal Diseases

Scanning laser ophthalmoscopes (SLOs) can be used for early detection of retinal diseases. With the advent of latest screening technology, the advantage of using SLO is its wide field of view, which can image a large part of the retina for better diagnosis of the retinal diseases. On the other hand, during the imaging process, artefacts such as eyelashes and eyelids are also imaged along with the retinal area. This brings a big challenge on how to exclude these artefacts. In this paper, we propose a novel approach to automatically extract out true retinal area from an SLO image based on image processing and machine learning approaches. To reduce the complexity of image processing tasks and provide a convenient primitive image pattern, we have grouped pixels into different regions based on the regional size and compactness, called superpixels. The framework then calculates image based features reflecting textural and structural information and classifies between retinal area and artefacts. The experimental evaluation results have shown good performance with an overall accuracy of 92%.

Automatic Detection and Severity Assessment of Crop Diseases Using Image Pattern Recognition

Disease diagnosis and severity assessment are necessary and critical for predicting the likely crop yield losses, evaluating the economic impact of the disease, and determining whether preventive treatments are worthwhile or particular control strategies could be taken. In this work, we propose to make advances in the field of automatic detection and diagnosis and severity assessment of crop diseases using image pattern recognition. We have developed a two-stage crop disease pattern recognition system which can automatically identify crop diseases and assess sevrity based on combination of marker-controlled watershed segmentation, superpixel based feature analysis and classification. We have conducted experimental evaluation using different feature selection and classification methods. The experimental result shows that the proposed approach can accurately detect crop diseases (i.e. Septoria and Yellow rust, which are the two most important and major types of wheat diseases in UK and across the world) and assess the disease severity with efficient processing speed.

A novel computer vision-based approach to automatic detection and severity assessment of crop diseases

Accurate detection and identification of crop diseases plays an important role in effectively controlling and preventing diseases for sustainable agriculture and food security. In this work, we have developed a novel computer vision-based approach for automatically identifying crop diseases based on marker-controlled watershed segmentation, superpixel based feature analysis and classification. The experimental result demonstrates that the proposed approach can accurately detect crop diseases (i.e. Septoria and Yellow rust. Two types of most important and major wheat diseases in UK and across the world) and assess the disease severity with efficient processing speed.

AUTOMATIC EXTRACTION OF THE OPTIC DISC BOUNDARY FOR DETECTING RETINAL DISEASES

In this paper, we propose an algorithm based on active shape model for the extraction of Optic Disc boundary. The determination of Optic Disc boundary is fundamental to the automation of retinal eye disease diagnosis because the Optic Disc Center is typically used as a reference point to locate other retinal structures, and any structural change in Optic Disc, whether textural or geometrical, can be used to determine the occurrence of retinal diseases such as Glaucoma. The algorithm is based on determining a model for the Optic Disc boundary by learning patterns of variability from a training set of annotated Optic Discs. The model can be deformed so as to reflect the boundary of Optic Disc in any feasible shape. The algorithm provides some initial steps towards automation of the diagnostic process for retinal eye disease in order that more patients can be screened with consistent diagnoses. The overall accuracy of the algorithm was 92% on a set of 110 images.

A Novel Adaptive Deformable Model for Automated Optic Disc and Cup Segmentation to Aid Glaucoma Diagnosis

This paper proposes a novel Adaptive Region-based Edge Smoothing Model (ARESM) for automatic boundary detection of optic disc and cup to aid automatic glaucoma diagnosis. The novelty of our approach consists of two aspects: 1) automatic detection of initial optimum object boundary based on a Region Classification Model (RCM) in a pixel-level multidimensional feature space; 2) an Adaptive Edge Smoothing Update model (AESU) of contour points (e.g. misclassified or irregular points) based on iterative force field calculations with contours obtained from the RCM by minimising energy function (an approach that does not require predefined geometric templates to guide auto-segmentation). Such an approach provides robustness in capturing a range of variations and shapes. We have conducted a comprehensive comparison between our approach and the state-of-the-art existing deformable models and validated it with publicly available datasets. The experimental evaluation shows that the proposed approach significantly outperforms existing methods. The generality of the proposed approach will enable segmentation and detection of other object boundaries and provide added value in the field of medical image processing and analysis.

An Automated Cloud-Based Big Data Analytics Platform for Customer Insights

Product reviews have a significant influence on strategic decisions for both businesses and customers on what to produce or buy. However, with the availability of large amounts of online information, manual analysis of reviews is costly and time consuming, as well as being subjective and prone to error. In this work, we present an automated scalable cloud-based system to harness big customer reviews on products for gaining customer insights through data pipeline from data acquisition, analysis to visualisation in an efficient way. The experimental evaluation has shown that the proposed system achieves good performance in terms of accuracy and computing time.

Glaucoma classification using Regional Wavelet Features of the ONH and its surroundings

Glaucoma is one of the leading cause of blindness but the detection at its earliest stage and subsequent treatment can aid patients to preserve blindness. The existing work has been focusing on global features such as texture, grayscale and wavelet energy of the Optic Nerve Head (ONH) and its surrounding to differentiate between normal and glaucoma images. In contrast to previous approaches which focus on global information only, this work proposes a new approach to automatically classify between the normal and glaucoma images based on Regional Wavelet Features of the ONH and different regions around it. These regions are usually used for diagnosis of glaucoma by clinicians through visual observation only. Our method automatically determines different clinically observed regions around the ONH and performs classification on the basis of wavelet energy at different frequency subbands. We have conducted experiments based on different global features and regional features respectively and applied it to RIMONE (An Open Retinal Image Database for Optic Nerve Evaluation) database with 158 images. The experimental evaluation demonstrated that the classification accuracy of normal and glaucoma images using Regional Wavelet Features of different regions with 93% outperforms all other feature sets.

Superpixel Based Retinal Area Detection in SLO Images

Distinguishing true retinal area from artefacts in SLO images is a challenging task, which is the first important step towards computer-aided disease diagnosis. In this paper, we have developed a new method based on superpixel feature analysis and classification approaches for determination of retinal area scanned by Scanning Laser Ophthalmoscope(SLO). Our prototype has achieved the accuracy of 90% on healthy as well as diseased retinal images. To the best of our knowledge, this is the first work on retinal area detection in SLO images.