This study proposes a classification framework for Alzheimer's Disease (AD) using statistical features from displacement vector fields and Jacobian determinants computed from structural magnetic resonance imaging (MRI). Images from 542 cognitively normal (CN), 341 mild cognitive impairment (MCI), and 245 AD individuals were analyzed. Groupwise registration and deformable coregistration quantified spatial deformations and local volumetric changes. Statistical moments from displacement vector fields and Jacobian determinants enabled regionspecific analysis. Stratified by sex, CN vs. AD classification achieved an AUC of 0.93 and 88.68 % accuracy for males, and an AUC of 0.94 with 87.89 % accuracy for females, demonstrating the efficacy of deformation-based biomarkers for AD diagnosis.

Early detection of Alzheimer's disease (AD) is critical for timely intervention, and neuroimaging biomarkers play a fundamental role in assessing structural brain changes. The Koedam visual scale is a widely used tool for evaluating parietal atrophy, particularly in early-onset AD. This study presents an automated approach to Koedam scale classification using T1-weighted MRI features and clustering techniques. The proposed method follows a structured pipeline, including skull stripping, noise reduction, bias field correction, and region of interest (ROI) selection. Brain tissue segmentation is performed using a probabilistic model-based approach, classifying image voxels into gray matter, white matter, and cerebrospinal fluid. Additionally, deformation fields derived from nonlinear image registration with a non-atrophied template are extracted to capture structural differences associated with atrophy. The strain tensor, derived from the displacement field, is computed to further characterize tissue deformation. A feature selection step is applied before clustering, where a Gaussian Mixture Model (GMM) clustering algorithm is used to categorize images into four Koedam atrophy levels, mimicking expert visual assessment. The method was evaluated on a dataset of 103 MRI images, demonstrating a clear differentiation between atrophy severity levels. The resulting clusters exhibited progressively decreasing mean Mini-Mental State Examination (MMSE) values:$22.91 \pm 4.98$for cluster 0,$22.07 \pm 3.60$for cluster 1, 20.76$\pm 3.47$for cluster 2, and$19.84 \pm 0.14$for cluster 3. These findings indicate that the proposed approach effectively quantifies parietal atrophy, providing an objective and reproducible alternative to expert visual assessment.

Alzheimer's disease (AD) is the most common cause of dementia, accounting for 60 to 80% of all cases. Because of population aging, this disease has become one of the most relevant global public health problems. Several studies have shown the hippocampal structures present significant asymmetry in AD, and that difference, measured from the volumes between left and right hippocampus, varies with the disease progression. Although imaging biomarkers have been proposed to investigate whether the asymmetry of hippocampus subfields changes through the disease progression, little attention has been paid to explore asymmetrical hippocampal image features to aid for AD early diagnosis. In this study, we propose a new method for the classification of Magnetic Resonance (MR) images in both cognitively normal (CN) versus mild cognitive impairment (MCI) and CN versus mild-AD patient groups using only asymmetrical features extracted from the hippocampal MR image hemispheres. The features, devised from the magnitude response images resulting from applying 3-D log-Gabor filters to an MR input image, are used to train Support Vector Machine classifiers for the MR image classification. Quantitative evaluation of our proposed method applied to MR image classification resulted in accuracy, F1-score, and AUC average values of 71.23%, 0.67, and 0.77 for the CNxMCI case, and 80.43%, 0.75, and 0.88 for the CNxAD case. These results are very promising, considering we used only asymmetrical features from the hippocampal regions in this study.

The midsagittal plane (MSP) separates the cerebrum into left and right hemispheres and its detection has a number of useful applications in brain image processing. We propose an automatic technique for the detection of the MSP in magnetic resonance (MR) images that uses a sheetness measure obtained from eigenanalysis of local matrix of second-order moments of 3D phase congruency responses to determine those voxels most likely to belong to the MSP. A weighted least-squares fitting algorithm is used in a coarse-to-fine iterative manner to find the best fitting plane (the MSP) through the selected voxels. Unlike most of the proposed approaches, which are mainly based on symmetric measures of the brain, our technique uses a direct measure to find the MSP. Our technique was applied to 202 MR images (40 clinical and 162 synthetic) and it has shown to be very effective for both symmetrical and asymmetrical brain images. Quantitative assessment, using the angle between unit normals of the detected and reference MSPs, yielded to a mean absolute angle value bellow 0.5°.

The pectoral muscle represents a predominant density region in most medio-lateral oblique (MLO) views of mammograms; its inclusion can affect the results of intensity-based image processing methods or bias procedures in the detection of breast cancer. Local analysis of the pectoral muscle may be used to identify the presence of abnormal axillary lymph nodes, which may be the only manifestation of occult breast carcinoma. We propose a new method for the identification of the pectoral muscle in MLO mammograms based upon a multiresolution technique using Gabor wavelets. This new method overcomes the limitation of the straight-line representation considered in our initial investigation using the Hough transform. The method starts by convolving a group of Gabor filters, specially designed for enhancing the pectoral muscle edge, with the region of interest containing the pectoral muscle. After computing the magnitude and phase images using a vector-summation procedure, the magnitude value of each pixel is propagated in the direction of the phase. The resulting image is then used to detect the relevant edges. Finally, a post-processing stage is used to find the true pectoral muscle edge. The method was applied to 84 MLO mammograms from the Mini-MIAS (Mammographic Image Analysis Society, London, U.K.) database. Evaluation of the pectoral muscle edge detected in the mammograms was performed based upon the percentage of false-positive (FP) and false-negative (FN) pixels determined by comparison between the numbers of pixels enclosed in the regions delimited by the edges identified by a radiologist and by the proposed method. The average FP and FN rates were, respectively, 0.58% and 5.77%. Furthermore, the results of the Gabor-filter-based method indicated low Hausdorff distances with respect to the hand-drawn pectoral muscle edges, with the mean and standard deviation being 3.84 +/- 1.73 mm over 84 images.

This paper presents a procedure for the analysis of left-right (bilateral) asymmetry in mammograms. The procedure is based upon the detection of linear directional components by using a multiresolution representation based upon Gabor wavelets. A particular wavelet scheme with two-dimensional Gabor filters as elementary functions with varying tuning frequency and orientation, specifically designed in order to reduce the redundancy in the wavelet-based representation, is applied to the given image. The filter responses for different scales and orientation are analyzed by using the Karhunen-Loève (KL) transform and Otsu's method of thresholding. The KL transform is applied to select the principal components of the filter responses, preserving only the most relevant directional elements appearing at all scales. The selected principal components, thresholded by using Otsu's method, are used to obtain the magnitude and phase of the directional components of the image. Rose diagrams computed from the phase images and statistical measures computed thereof are used for quantitative and qualitative analysis of the oriented patterns. A total of 80 images from 20 normal cases, 14 asymmetric cases, and six architectural distortion cases from the Mini-MIAS (Mammographic Image Analysis Society, London, U.K.) database were used to evaluate the scheme using the leave-one-out methodology. Average classification accuracy rates of up to 74.4% were achieved.