Jagadish Nayak

Automated Diagnosis of Glaucoma Using Digital Fundus Images

Glaucoma is a disease of the optic nerve caused by the increase in the intraocular pressure of the eye. Glaucoma mainly affects the optic disc by increasing the cup size. It can lead to the blindness if it is not detected and treated in proper time. The detection of glaucoma through Optical Coherence Tomography (OCT) and Heidelberg Retinal Tomography (HRT) is very expensive. This paper presents a novel method for glaucoma detection using digital fundus images. Digital image processing techniques, such as preprocessing, morphological operations and thresholding, are widely used for the automatic detection of optic disc, blood vessels and computation of the features. We have extracted features such as cup to disc (c/d) ratio, ratio of the distance between optic disc center and optic nerve head to diameter of the optic disc, and the ratio of blood vessels area in inferior-superior side to area of blood vessel in the nasal-temporal side. These features are validated by classifying the normal and glaucoma images using neural network classifier. The results presented in this paper indicate that the features are clinically significant in the detection of glaucoma. Our system is able to classify the glaucoma automatically with a sensitivity and specificity of 100% and 80% respectively.

Simultaneous storage of medical images in the spatial and frequency domain: A comparative study

BackgroundDigital watermarking is a technique of hiding specific identification data for copyright authentication. This technique is adapted here for interleaving patient information with medical images, to reduce storage and transmission overheads.MethodsThe patient information is encrypted before interleaving with images to ensure greater security. The bio-signals are compressed and subsequently interleaved with the image. This interleaving is carried out in the spatial domain and Frequency domain. The performance of interleaving in the spatial, Discrete Fourier Transform (DFT), Discrete Cosine Transform (DCT) and Discrete Wavelet Transform (DWT) coefficients is studied. Differential pulse code modulation (DPCM) is employed for data compression as well as encryption and results are tabulated for a specific example.ResultsIt can be seen from results, the process does not affect the picture quality. This is attributed to the fact that the change in LSB of a pixel changes its brightness by 1 part in 256. Spatial and DFT domain interleaving gave very less %NRMSE as compared to DCT and DWT domain.ConclusionThe Results show that spatial domain the interleaving, the %NRMSE was less than 0.25% for 8-bit encoded pixel intensity. Among the frequency domain interleaving methods, DFT was found to be very efficient.

Enhancement of retinal fundus Image to highlight the features for detection of abnormal eyes

Medical image analysis to aid in clinical diagnosis is one of the research areas currently drawing intense interests of scientists and physicians. The retinal fundus photograph are widely used in the diagnosis and treatment of various eye diseases such as Diabetic Retinopathy, glaucoma etc. Diabetic Retinopathy is the leading cause of blindness in the working age population. If the disease is detected and treated early, many of the visual losses can be prevented. This paper describes the methods to detect main features of fundus images such as optic disk, fovea, and exudates and blood vessels. To determine the optic Disk and its centre we find the brightest part of the fundus and apply Hough transform. The candidate region of fovea is defined an area circle. The detection of fovea is done by using its spatial relationship with optic disk. Exudates are found using their high grey level variation and their contours are determined by means of morphological reconstruction techniques. The blood vessels are highlighted using bottom hat transform and morphological dilation after edge detection. All the enhanced features are then combined in the fundus image for the detection of abnormalities in the eye

Automatic identification of diabetic maculopathy stages using fundus images.

Diabetes mellitus is a major cause of visual impairment and blindness. Twenty years after the onset of diabetes, almost all patients with type 1 diabetes and over 60% of patients with type 2 diabetes will have some degree of retinopathy. Prolonged diabetes retinopathy leads to maculopathy, which impairs the normal vision depending on the severity of damage of the macula. This paper presents a computer-based intelligent system for the identification of clinically significant maculopathy, non-clinically significant maculopathy and normal fundus eye images. Features are extracted from these raw fundus images which are then fed to the classifier. Our protocol uses feed-forward architecture in an artificial neural network classifier for classification of different stages. Three different kinds of eye disease conditions were tested in 350 subjects. We demonstrated a sensitivity of more than 95% for these classifiers with a specificity of 100%, and results are very promising. Our systems are ready to run clinically on large amounts of datasets.

Efficient Storage and Transmission of Digital Fundus Images with Patient Information Using Reversible Watermarking Technique and Error Control Codes

Handling of patient records is increasing overhead costs for most of the hospitals in this digital age. In most hospitals and health care centers, the patient text information and corresponding medical images are stored separately as different files. There is a possibility of mishandling the text file containing patient history. We are proposing a novel method for the compact storage and transmission of patient information with the medical images. In this technique, we are using a reversible watermarking technique to hide the patient information within the retinal fundus image. There is a possibility that these medical images, which carry patient information, can get corrupted by the noise during the storage or transmission. The safe recovery of patient information is important in this situation. So, to recover the maximum amount of text information in the noisy environment, the encrypted patient information is coded with error control coding (ECC) techniques. The performance of three types of ECC for various levels of salt & pepper (S & P) noise is tabulated for a specific example. The proposed system is more reliable even in a noisy environment and saves memory.

Reliable transmission and storage of medical images with patient information using error control codes

A new method for compact storage and transmission of medical images with concealed patient information in noisy environment is evinced. Digital watermarking is the technique adapted here for interleaving patient information with medical images. The patient information, which comprises of text data and signal graph, is encrypted to prevent unauthorized access of data. The latest encryption algorithm (Rijndael) is used for encrypting the text information. Signal graphs (ECG, EEG EMG etc) are compressed using DPCM technique. To enhance the robustness of the embedded information, the patient information is coded by error correcting codes (ECC) Reed Solomon (RS) codes. The noisy scenario is simulated by adding salt and pepper (S&P) noise to the embedded image. For different signal to noise ratio (SNR) of the image, bit error rate (BER) and number of character altered (NOCA) for text data and percentage distortion (PDIST) for the signal graph is evaluated. It is elicited that coded systems can perform better than the uncoded systems.

Identification of voice disorders using speech samples

This paper attempts to identify pathological disorders of larynx using wavelet analysis. Speech samples carry symptoms of disorder in the place of their origin. The speech signal is subjected to wavelet analysis, and the coefficients are used to identify disorders such as vocal fold paralysis. Multilayer artificial neural network is used for classification of normal and affected signals.

Identification of cataract and post-cataract surgery optical images using artificial intelligence techniques.

Human eyes are most sophisticated organ, with perfect and interrelated subsystems such as retina, pupil, iris, cornea, lens and optic nerve. The eye disorder such as cataract is a major health problem in the old age. Cataract is formed by clouding of lens, which is painless and developed slowly over a long period. Cataract will slowly diminish the vision leading to the blindness. At an average age of 65, it is most common and one third of the people of this age in world have cataract in one or both the eyes. A system for detection of the cataract and to test for the efficacy of the post-cataract surgery using optical images is proposed using artificial intelligence techniques. Images processing and Fuzzy K-means clustering algorithm is applied on the raw optical images to detect the features specific to three classes to be classified. Then the backpropagation algorithm (BPA) was used for the classification. In this work, we have used 140 optical image belonging to the three classes. The ANN classifier showed an average rate of 93.3% in detecting normal, cataract and post cataract optical images. The system proposed exhibited 98% sensitivity and 100% specificity, which indicates that the results are clinically significant. This system can also be used to test the efficacy of the cataract operation by testing the post-cataract surgery optical images.

COMPUTER-BASED IDENTIFICATION OF CATARACT AND CATARACT SURGERY EFFICACY USING OPTICAL IMAGES

The eyes are complex sensory organs, they are designed to capture images under varying light conditions. Eye disorders, such as cataract, among the elderly are a major health problem. Cataract is a painless clouding of the eye lens which develops over a long period of time. During this time, the eyesight gradually worsens. It can eventually lead to blindness and, is common in older people. In fact, about a third of people over 65 have cataracts in one or both eyes. In this paper, we made use of two types of classifiers for identification of normal, cataract (early and developed stage), and post-cataract eyes using features extracted from optical images. These classifiers are artificial neural network and support vector machine. A database of 174 subjects, using the cross-validation strategy, is used to test the effectiveness of both classifiers. We demonstrate a sensitivity of more than 90% for both of these classifiers. Furthermore, they have a specificity of 100% and, as such, the results obtained are very promising. The proposed feature extraction and classification systems are ready clinically to run on a large amount of data sets.

Electroglottographic Signal Acquisition and Neural Network based classification for pathology

The method is used to register the laryngeal behavior indirectly by measuring change in the electrical impedance across the throat during speak or voice. The RF carrier signal is amplitude modulated by the modulating speech/ voice signal and the dc component from the demodulated signal is extracted. The variations in the dc component corresponds to the vocal fold abduction/laryngeal movement. For normal and pathology conditions the results are recorded. These values form a feature vector which reveal information regarding pathology. Then a classical multilayer feed forward neural network with back propagation algorithm is employed to serve as a classifier of the feature vector, giving 100% successful results for the specific data set considered.