Muhammad Mohsin Riaz

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Singular value decomposition based through wall image enhancement is proposed which is capable of discriminating target, noise and clutter signals. The overlapping boundaries of clutter, noise and target signals are separated using fuzzy c-means clustering algorithm. Moreover, weights are assigned to different singular values based on their membership(More)
Adrenal Myelolipoma is a rare benign neoplasm composed of mature adipose tissue and a variable amount of haemopoietic elements. Most lesions are small and asymptomatic, discovered incidentally during autopsy or on imaging studies performed for other reasons. Two cases of myelolipoma are presented here, where the tumours were hormonally inactive, but(More)
Singular value decomposition and information theoretic criterion based clutter reduction is proposed for ground penetrating radar imaging. The scheme is capable of discriminating target, clutter and noise subspaces. Information theoretic criterion is used with conventional singular value decomposition to find the target singular values. The proposed scheme(More)
Singular value decomposition based through wall image enhancement is proposed which is capable of discriminating target, noise and clutter signals. The overlapping boundaries of clutter, noise and target signals are separated using fuzzy logic. Fuzzy inference engine is used to assign weights to different spectral components. K-means clustering is used for(More)
An image fusion technique for magnetic resonance imaging (MRI) and positron emission tomography (PET) using local features and fuzzy logic is presented. The aim of proposed technique is to maximally combine useful information present in MRI and PET images. Image local features are extracted and combined with fuzzy logic to compute weights for each pixel.(More)
A through wall image enhancement scheme based on Takagi-Sugeno fuzzy system and principal component analysis is proposed. The scheme incorporates spectral properties of image and textural properties of eigen components of image to assign weights. The scheme overcomes the empirical setting of inference engine and output membership functions. Simulation(More)
A technique for magnetic resonance brain image classification using perceptual texture features, fuzzy weighting and support vector machine is proposed. In contrast to existing literature which generally classifies the magnetic resonance brain images into normal and abnormal classes, classification with in the abnormal brain which is relatively hard and(More)
Improved guided image fusion for magnetic resonance and computed tomography imaging is proposed. Existing guided filtering scheme uses Gaussian filter and two-level weight maps due to which the scheme has limited performance for images having noise. Different modifications in filter (based on linear minimum mean square error estimator) and weight maps (with(More)