Computer-aided diagnostic (CAD) systems provide fast and reliable diagnosis for medical images. In this paper, CAD system is proposed to analyze and automatically segment the lungs and classify each lung into normal or cancer. Using 70 different patients' lung CT dataset, Wiener filtering on the original CT images is applied firstly as a preprocessing step. Secondly, we combine histogram analysis with thresholding and morphological operations to segment the lung regions and extract each lung separately. Amplitude-Modulation Frequency-Modulation (AM-FM) method thirdly, has been used to extract features for ROIs. Then, the significant AM-FM features have been selected using Partial Least Squares Regression (PLSR) for classification step. Finally, K-nearest neighbour (KNN), support vector machine (SVM), naïve Bayes, and linear classifiers have been used with the selected AM-FM features. The performance of each classifier in terms of accuracy, sensitivity, and specificity is evaluated. The results indicate that our proposed CAD system succeeded to differentiate between normal and cancer lungs and achieved 95% accuracy in case of the linear classifier.
Diagnosis of Diabetic Macular Edema (DME) from Fundus Fluorescein Angiography (FFA) image sequences is a standard clinical practice. Nevertheless, current methods depend on subjective evaluation of the amount of fluorescein leakage in the images which lack reproducibility and require well-trained grader. In this work, we present a method for processing FFA images to generate a fluorescein leakage map that can be used for quantitative evaluation of DME. An essential step in the proposed method is to model the spatial distribution of the image intensity within the macula. This model, which represents the non-leaking structures, is then subtracted from the late timeframe image to enhance the areas of fluorescein leakage. The resulting difference image is then mapped with empirical linear transformation to produce a color Fluorescein Leakage Map (FLM) that can be used for quantitative assessment and detection of DME.The method was applied to 13 image sequences for 13 different patients. The resulting FLM maps were found to be correlated with the thickness maps produced by Optical Coherence Tomography (OCT). The relatively high correlation between the FLM and OCT maps show the potential and of using the developed method for quantitatively assess the DME in FFA image sequences.
Fundus fluorescein angiography (FFA) is a standard screening and diagnosis technique for several retinal diseases. The analysis of FFA images is performed qualitatively by skilled observers, and thus is vulnerable to inter-and intra-observer variability. In this study, the authors present a method for computer-aided analysis of FFA images. The method is based on generating quantitative colour fluorescein leakage maps (FLM) that mimic the thickness maps generated by the optical coherence tomography (OCT). Results from 64 patients show strong correlation between the FLM and OCT thickness maps (r = 0.8). The method was found to be reproducible and robust to variability in the image acquisition times.
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