Simulation Based Analysis of Automated Classification of Medical Images

Automated Diagnosis of Glaucoma Using Empirical Wavelet Transform and Correntropy Features Extracted From Fundus Images

Glaucoma is an ocular disorder caused due to increased fluid pressure in the optic nerve. It damages the optic nerve and subsequently causes loss of vision. The available scanning methods are Heidelberg retinal tomography, scanning laser polarimetry, and optical coherence tomography. These methods are expensive and require experienced clinicians to use them. So, there is a need to diagnose glaucoma accurately with low cost. Hence, in this paper, we have presented a new methodology for an automated diagnosis of glaucoma using digital fundus images based on empirical wavelet transform (EWT). The EWT is used to decompose the image, and correntropy features are obtained from decomposed EWT components. These extracted features are ranked based on t value feature selection algorithm. Then, these features are used for the classification of normal and glaucoma images using least-squares support vector machine (LS-SVM) classifier. The LS-SVM is employed for classification with radial basis function, Morlet wavelet, and Mexican-hat wavelet kernels. The classification accuracy of the proposed method is 98.33% and 96.67% using threefold and tenfold cross validation, respectively.

Event-based progression detection strategies using scanning laser polarimetry images of the human retina

Monitoring glaucoma patients and ensuring optimal treatment requires accurate and precise detection of progression. Many glaucomatous progression detection strategies may be formulated for Scanning Laser Polarimetry (SLP) data of the local nerve fiber thickness. In this paper, several strategies, all based on repeated GDx VCC SLP measurements, are tested to identify the optimal one for clinical use. The parameters of the methods were adapted to yield a set specificity of 97.5% on real image series. For a fixed sensitivity of 90%, the minimally detectable loss was subsequently determined for both localized and diffuse loss. Due to the large size of the required data set, a previously described simulation method was used for assessing the minimally detectable loss. The optimal strategy was identified and was based on two baseline visits and two follow-up visits, requiring two-out-of-four positive tests. Its associated minimally detectable loss was 5-12 μm, depending on the reproducibility of the measurements.