Image Histogram Research Articles

Histogram analysis in diffusion-weighted imaging in differentiating breast masses

Abstract Background Gray-scale histogram analysis has been submitted to evaluate the heterogeneity of the diffusion distribution among different sorts of tumors in the body. Measures obtained from apparent diffusion coefficient (ADC) histograms reflect the histopathological heterogeneity, distributions of cellular density, and tissue degeneration. This can supply a more credible base for recognition, categorization, and prognosis assessment of benign and malignant tumors. The aim of this work was to assess the role of ADC histogram analysis in differentiating benign from malignant breast lesions. Results Among ADC histogram parameters, there was significant difference between benign and malignant lesions regarding to ADC mean being 1.59 ± 0.32 for benign tumors versus 0.871 ± 0.29 for malignant tumors (P value < 0.001), ADC minimum being 1.09 ± 0.44 for benign lesions versus 0.432 ± 0.327 malignant lesions (P value < 0.001), ADC maximum being 1.92 ± 0.387 for benign lesions versus 1.27 ± 0.390 for malignant lesions (P value < 0.001), and kurtosis being 3.71 ± 2.54 for benign lesions versus 6.23 ± 3.82 for malignant lesions (P value = 0.007). Among ADC histogram parameters, ADC mean had the highest diagnostic performance with AUC (0.959), specificity (95.7%), and accuracy (93.3%). Conclusion ADC histogram analysis is used as sensitive and specific technique in differentiating benign from malignant breast lesions with ADC mean showing the highest diagnostic performance among ADC histogram parameters.

Prediction of vertebral body mechanical parameters using opportunistic CT data

Abstract Introduction: The purpose of the study was to test a method describing the mechanical properties of bone using clinically available CT data. Material and Methods: The samples, 50 L3 vertebrae taken from males 22 to 81 years old, were examined with dual-energy X-ray absorptiometry and quantitative CT. An analysis of CT images and their image histograms was performed. The greyscale means – XC1, XC2, their standard deviations – SD1, SD2, and the areas under the curves – X1, X2 characterizing the organic matrix and bone material, respectively, were calculated by fitting two Gaussian functions. The compression tests were performed to determine the elastic modulus (E), ultimate stress (σmax), ultimate strain, and the ratio of work to fracture and the volume of the vertebra. Results: It was found that E and σmax were most precisely described by the parameter related to the trabecular bone density (XC2) obtained from the histogram analysis. Using the linear model, the coefficient of determination (R2) equals to 0.706 and 0.846 for E and σmax, respectively. For volumetric (vBMD) and areal bone mineral density (aBMD), R2 is 0.641 and 0.208 for E, while for σmax equals 0.784 and 0.356. After correction of vBMD using the histogram parameters R2 for E and σmax rise to 0.692 and 0.835, respectively. Conclusions: The superiority of the new method of E and σmax estimation based on clinically available CT data was confirmed. The proposed method does not require calibration and predicts the mechanical parameters of the vertebrae more precisely than vBMD or aBMD separately.

Low illumination image enhancement based on improved CLAHE algorithm

Abstract In the case of low light, the target features will be weakened or even disappear, thus affecting the target detection effect based on aerial images. In order to improve the image effect, an improved CLAHE algorithm is designed. In the process of image enhancement, the CLAHE algorithm is improved according to image pixel size and histogram characteristics, which can improve the smoothness of CLAHE algorithm for uniform color parts. The experimental results show that the improved CLAHE algorithm is better than other histogram equalization algorithms, and the method has low cost and practical application value.

Automatic exposure strategy network for robust visual odometry in environments with high dynamic range

The success of visual-based localization requires the image features to be sufficiently captured in well-exposed images and reliably tracked among image sequences. In this paper, a learning-based exposure strategy algorithm is developed to improve the performance of visual odometry or simultaneous localization and mapping in scenes with sudden illumination changes and high dynamic range (HDR). The exposure value is predicted by optimizing a novel image information metric, which is calculated based on a multi-scale image histogram. Experimental observations show that the proposed metric is highly related to the localization performance, and achieves better localization accuracy when compared to other state-of-the-art metrics. Using this novel metric, the exposure control network is designed to predict the exposure value for the next image given a sequence of recently captured images as inputs. For different application scenarios, alternative network frameworks are proposed with the options to include an optimization module providing improved exposure values for feedback learning, but with additional computation complexity. The approach is light-weight for real-time video applications and the images captured using the predicted exposure values are well-exposed with better detected and matched features in a variety of challenging HDR scenes. Code will be available in https://github.com/leejieyu13/HDR-AE.

Domain‐independent adaptive histogram‐based features for pomegranate fruit and leaf diseases classification

AbstractDisease identification for fruits and leaves in the field of agriculture is important for estimating production, crop yield, and earnings for farmers. In the specific case of pomegranates, this is challenging because of the wide range of possible diseases and their effects on the plant and the crop. This study presents an adaptive histogram‐based method for solving this problem. Our method describe is domain independent in the sense that it can be easily and efficiently adapted to other similar smart agriculture tasks. The approach explores colour spaces, namely, Red, Green, and Blue along with Grey. The histograms of colour spaces and grey space are analysed based on the notion that as the disease changes, the colour also changes. The proximity between the histograms of grey images with individual colour spaces is estimated to find the closeness of images. Since the grey image is the average of colour spaces (R, G, and B), it can be considered a reference image. For estimating the distance between grey and colour spaces, the proposed approach uses a Chi‐Square distance measure. Further, the method uses an Artificial Neural Network for classification. The effectiveness of our approach is demonstrated by testing on a dataset of fruit and leaf images affected by different diseases. The results show that the method outperforms existing techniques in terms of average classification rate.

Comparison of Preprocessing Method Impact on the Detection of Soldering Splashes Using Different YOLOv8 Versions

Quality inspection of electronic boards during the manufacturing process is a crucial step, especially in the case of specific and expensive power electronic modules. Soldering splash occurrence decreases the reliability and electric properties of final products. This paper aims to compare different YOLOv8 models (small, medium, and large) with the combination of basic image preprocessing techniques to achieve the best possible performance of the designed algorithm. As preprocessing methods, contrast-limited adaptive histogram equalization (CLAHE) and image color channel manipulation are used. The results show that a suitable combination of the YOLOv8 model and preprocessing methods leads to an increase in the recall parameter. In our inspection task, recall can be considered the most important metric. The results are supported by a standard two-way ANOVA test.

The value of multiple diffusion metrics based on whole-lesion histogram analysis in evaluating the subtypes and proliferation status of non-small cell lung cancer.

Limited studies have explored the utility of whole-lesion histogram analysis in discerning the subtypes and proliferation status of non-small cell lung cancer (NSCLC), despite its potential to provide comprehensive tissue assessment through the computation of additional quantitative metrics. This study sought to assess the significance of intravoxel incoherent motion (IVIM) and diffusion kurtosis imaging (DKI) histogram parameters in discriminating between squamous cell carcinoma (SCC) and adenocarcinoma (AC), and to examine the correlation of each parameter with the proliferative marker Ki-67. Patients with space-occupying lesions detected by chest CT examination and with further routine MRI, DKI and IVIM functional sequence scans were enrolled. Based on the pathological results, seventy patients with NSCLC were selected and divided into AC and SCC groups. Histogram parameters of IVIM (D, D*, f) and DKI (Dapp, Kapp) were calculated, and the Mann-Whitney U test or independent samples t test was used to analyze the differences in each histogram parameter of the SCC and AC groups. Receiver operating characteristic (ROC) curves were used to evaluate the diagnostic performance of the histogram parameters. The correlation coefficient between histogram parameters and Ki-67 was calculated using Spearman's or Pearson's methods. The D 10th percentile, D 90th percentile, D mean, D median, Dapp 10th percentile, Dapp 90th percentile, Dapp mean, Dapp median, Dapp skewness, Dapp SD of the AC groups were significantly higher than those of the SCC groups, while the Kapp entropy and Kapp SD of the SCC groups were significantly higher than those of the AC groups. All the above differences were statistically significant (all P < 0.05). ROC curve analysis revealed that Dapp mean showed the best performance for differentiating AC from SCC lesions, with an area under the ROC curve of 0.832 (95% confidence interval [CI]: 0.707-0.919). But there was no statistically significant difference in diagnostic efficacy compared to other histogram parameters (all P>0.05). Dapp 90thpercentile, Dapp mean, Kapp skewnes showed a slight negative correlation with Ki-67 expression (r value -0.340, -0.287, -0.344, respectively; P< 0.05), while the other histogram parameters showed no significant correlation with Ki-67 (all P > 0.05). Our study demonstrates the utility of IVIM and DKI histogram analyses in differentiating NSCLC subtypes, particularly AC and SCC. Correlations with the Ki-67 index suggest that Dapp mean, Dapp 90th percentile, and Kapp skewness may serve as markers of tumor aggressiveness, supporting their use in NSCLC diagnosis and treatment planning.

Deep learning methods for phase segmentation in backscattered electron images of cement paste and SCM-blended systems

Quantitative microstructure analysis of supplementary cementitious material (SCM) - blended paste through backscattered electron (BSE) imaging has long been intractable owing to the sophisticated micro phase distribution and overlapped greyscale histogram. This study explores the use of Convolutional Neural Network (CNN) based supervised semantic segmentation methods for quantifying phase assemblage in BSE images of blank cement paste and SCM-blended cement pastes. Four types of SCMs, namely limestone, slag, quartz and metakaolin, were separately blended with WPC and OPC paste for analysis. U-Net architecture with and without ResNet backbones were trained to perform pixel-level image segmentation of anhydrous cement and SCM particles. The results indicate that deep learning models can robustly segment anhydrous cement particles from BSE images and achieve same level of precision as QXRD. For limestone, quartz and slag, deep learning models show strong potential for semi-quantitative segmentation. While metakaolin cannot be reliably segmented based solely on graphic information.

A fundus vessel segmentation method based on double skip connections combined with deep supervision.

Fundus vessel segmentation is vital for diagnosing ophthalmic diseases like central serous chorioretinopathy (CSC), diabetic retinopathy, and glaucoma. Accurate segmentation provides crucial vessel morphology details, aiding the early detection and intervention of ophthalmic diseases. However, current algorithms struggle with fine vessel segmentation and maintaining sensitivity in complex regions. Challenges also stem from imaging variability and poor generalization across multimodal datasets, highlighting the need for more advanced algorithms in clinical practice. This paper aims to explore a new vessel segmentation method to alleviate the above problems. We propose a fundus vessel segmentation model based on a combination of double skip connections, deep supervision, and TransUNet, namely DS2TUNet. Initially, the original fundus images are improved through grayscale conversion, normalization, histogram equalization, gamma correction, and other preprocessing techniques. Subsequently, by utilizing the U-Net architecture, the preprocessed fundus images are segmented to obtain the final vessel information. Specifically, the encoder firstly incorporates the ResNetV1 downsampling, dilated convolution downsampling, and Transformer to capture both local and global features, which upgrades its vessel feature extraction ability. Then, the decoder introduces the double skip connections to facilitate upsampling and refine segmentation outcomes. Finally, the deep supervision module introduces multiple upsampling vessel features from the decoder into the loss function, so that the model can learn vessel feature representations more effectively and alleviate gradient vanishing during the training phase. Extensive experiments on publicly available multimodal fundus datasets such as DRIVE, CHASE_DB1, and ROSE-1 demonstrate that the DS2TUNet model attains F1-scores of 0.8195, 0.8362, and 0.8425, with Accuracy of 0.9664, 0.9741, and 0.9557, Sensitivity of 0.8071, 0.8101, and 0.8586, and Specificity of 0.9823, 0.9869, and 0.9713, respectively. Additionally, the model also exhibits excellent test performance on the clinical fundus dataset CSC, with F1-score of 0.7757, Accuracy of 0.9688, Sensitivity of 0.8141, and Specificity of 0.9801 based on the weight trained on the CHASE_DB1 dataset. These results comprehensively validate that the proposed method obtains good performance in fundus vessel segmentation, thereby aiding clinicians in the further diagnosis and treatment of fundus diseases in terms of effectiveness and feasibility.

Investigation on intermittent flow characteristics in horizontal pipe by visualization measurement method

Accurate measurement and prediction of the intermittent flow characteristics in horizontal pipes is important for constructing multiphase flow models and ensuring pipe flow safety. In this paper, a quantitative image post-processing technique for intermittent flow characteristics based on gray histogram image similarity is proposed, which can realize the measurement of slug frequency. In addition, the technique also has the ability to classify a large number of images, and can quickly find the elongated bubble head, liquid film area and liquid slug area of intermittent flow. On the basis of this technique, combined with image post-processing methods such as gas–liquid interface feature analysis, a set of intermittent flow image processing technique with perfect route is formed. Based on this post-processing technique, the similarity image oscillation trajectories of plug flow and slug flow are obtained. There are differences in the similarity image oscillation trajectories of the two intermittent sub-flow patterns, and the similarity image of the plug flow has an obvious platform period and trailing rising line, which can be used as a basis for the classification of the two intermittent sub-flow patterns. A correlation for predicting the slug frequency of intermittent sub-flow patterns is developed. The accuracy of this slug frequency prediction correlation can be improved by about 10 % compared to not dividing the sub-flow patterns. When the mixture Froude number Frm is less than 5.0, the radial position of the elongated bubble head decreases linearly as the Frm increases. When the Frm is greater than 5.0, the elongated bubble head oscillates near the middle of the pipe. Prediction correlations for the radial position of the elongated bubble head and the slug velocity are established separately, and the maximum error is ± 10 %. The modified mixed Froude number is proposed, and based on this, a new prediction model for the transition from plug flow to slug flow is established.

CataEx: A multi-task export tool for the Google Earth Engine data catalog

Satellite imagery is provided by different missions such as ASTER, MODIS, Sentinel, Landsat, IKONOS, GeoEye, SPOT, WorldView, Pléaides, or RapidEye. One of the major encumbrances is the digital volume that satellite imagery claims during download, storage, and processing. This inconvenience has been overcome since 2010 by the Google Earth Engine, a cloud-based platform for global geospatial analysis dedicated to users who are not necessarily remote sensing specialists.However, compatibility with traditional desktop or web-based GIS software remains tricky as bringing satellite imagery from the Google Earth Engine to another software requires a coded export via JavaScript or Python.We present the multi-functional code tool CataEx in JavaScript to exemplify several essential types of computations (i.e., filtering of image collections, cloud masking, index and histogram generation, and layer creation) before exporting images as GeoTIFFs. CataEx is kept deliberately simple without much "sophisticated" code language to allow JavaScript beginners to get familiar with basic coding concepts and develop their own scripts.

Added value of histogram analysis of intravoxel incoherent motion and diffusion kurtosis imaging for the evaluation of complete response to neoadjuvant therapy in locally advanced rectal cancer

Abstract Objective To evaluate how intravoxel incoherent motion (IVIM) and diffusion kurtosis imaging (DKI) histogram analysis contribute to assessing complete response (CR) to neoadjuvant therapy (NAT) in locally advanced rectal cancer (LARC). Material and methods In this prospective study, participants with LARC, who underwent NAT and subsequent surgery, with adequate MR image quality, were enrolled from November 2021 to March 2023. Conventional MRI (T2WI and DWI), IVIM, and DKI were performed before NAT (pre-NAT) and within two weeks before surgery (post-NAT). Image evaluation was independently performed by two experienced radiologists. Pathological complete response (pCR) was used as the reference standard. An IVIM–DKI-added model (a combination of IVIM and DKI histogram parameters with T2WI and DWI) was constructed. Receiver operating characteristic (ROC) curves were generated to evaluate the diagnostic performance of conventional MRI and the IVIM–DKI-added model. Results A total of 59 participants (median age: 58.00 years [IQR: 52.00, 62.00]; 38 [64%] men) were evaluated, including 21 pCR and 38 non-pCR cases. The histogram parameters of DKI, including skewness of kurtosis post-NAT (post-KSkewness) and root mean squared of change ratio of diffusivity (Δ%DDKI-root mean squared), were entered into the IVIM–DKI-added model. The area under the ROC curve (AUC) of the IVIM–DKI-added model for assessing CR to NAT was significantly higher than that of conventional MRI (0.855 [95% CI: 0.749–0.960] vs 0.685 [95% CI: 0.565–0.806], p &lt; 0.001). Conclusion IVIM and DKI provide added value in the evaluation of CR to NAT in LARC. Key Points QuestionThe current conventional imaging evaluation system lacks adequacy for assessing CR to NAT in LARC. FindingsSignificantly improved diagnostic performance was observed with the histogram analysis of IVIM and DKI in conjunction with conventional MRI. Clinical relevanceIVIM and DKI provide significant value in evaluating CR to NAT in LARC, which bears significant implications for reducing surgical complications and facilitating organ preservation.

SAR Ship Detection Using Image Histograms and Machine Learning Approach

SAR Ship Detection Using Image Histograms and Machine Learning Approach

Enhanced diagnostic accuracy for multiple lung diseases using a fine-tuned MobileNetV2 model with advanced pre-processing techniques

Early diagnosis of lung conditions is crucial for effective treatment and improving patient health. However, traditional diagnostic methods using chest X-ray images have some notable drawbacks, such as overlapping anatomical structures obscuring areas of interest, the presence of noise potentially masking abnormalities, and low contrast diminishing differentiation. In this research undertaking, we explored an enhanced MobileNetV2 approach to augmenting the accuracy of diagnosing multiple concomitant lung pathologies. We employed an inclusive methodology, incorporating several progressive steps. We leveraged contrast-limited adaptive histogram equalization to heighten the clarity of the dataset’s images. Bilateral filtering was applied to refine contrast and sharpness, along with utilizing a dense convolutional neural network. Additional techniques were utilized as well, such as image standardization, Gaussian blur, and histogram equalization, to further increase contrast and reduce noise. This was performed through rotations, scaling, horizontal flipping, brightness adjustment, elastic transformations, and random cropping and padding to generate more realistic exemplars. We defined and compiled a purpose-specific MobileNetV2 architecture for this research endeavor. The model was evaluated based on several metrics, including accuracy, precision, recall, F1-score, and specificity, after every epoch, to refine the training process. We used Bayesian optimization for more efficient fine-tuning of the model. Conditional random fields and ensemble averaging methods were employed for postprocessing. These enhancements led to superior results. After image enhancement, the model achieved an accuracy of 99.20 %, with a precision of 98.90 %, a recall of 99.10 %, and an F1-score of 99.00 %. The specificity of the predictions reached 99.40 %. These improvements assist medical workers in making more informed decisions. However, it is important to note that the current algorithm does not influence the effectiveness of treatment or recovery from the condition.

Desarrollo de un método de clasificación de pastas cerámicas basado en el análisis de imágenes de radiografía digital

X-rays have motivated the application of imaging techniques in Heritage Sciences, providing a tool that produces global information about the object. The radiography analysis generates information with physical meaning relating the material properties with the photon attenuation. In this work, the characterization of a digital radiography system is presented, and the basis of an analysis method for ceramics sherds is described, based on the study of histograms, which represent the distribution of pixel intensities that made the image. Thus, this study aims to provide a tool, based on a mathematical analysis, encompassing a Lorentzian fit to each image histogram and a cluster analysis, that leads to establish a contrast between ceramic pastes. By applying this method, we could distinguish six paste groups from a set of 275 ceramic objects (archaeological and contemporary) from the Maya region.

Comparative analysis of features and classification techniques in breast cancer detection for Biglycan biomarker images.

Breast cancer (BC) is considered the world's most prevalent cancer. Early diagnosis of BC enables patients to receive better care and treatment, hence lowering patient mortality rates. Breast lesion identification and classification are challenging even for experienced radiologists due to the complexity of breast tissue and variations in lesion presentations. This work aims to investigate appropriate features and classification techniques for accurate breast cancer detection in 336 Biglycan biomarker images. The Biglycan biomarker images were retrieved from the Mendeley Data website (Repository name: Biglycan breast cancer dataset). Five features were extracted and compared based on shape characteristics (i.e., Harris Points and Minimum Eigenvalue (MinEigen) Points), frequency domain characteristics (i.e., The Two-dimensional Fourier Transform and the Wavelet Transform), and statistical characteristics (i.e., histogram). Six different commonly used classification algorithms were used; i.e., K-nearest neighbours (k-NN), Naïve Bayes (NB), Pseudo-Linear Discriminate Analysis (pl-DA), Support Vector Machine (SVM), Decision Tree (DT), and Random Forest (RF). The histogram of greyscale images showed the best performance for the k-NN (97.6%), SVM (95.8%), and RF (95.3%) classifiers. Additionally, among the five features, the greyscale histogram feature achieved the best accuracy in all classifiers with a maximum accuracy of 97.6%, while the wavelet feature provided a promising accuracy in most classifiers (up to 94.6%). Machine learning demonstrates high accuracy in estimating cancer and such technology can assist doctors in the analysis of routine medical images and biopsy samples to improve early diagnosis and risk stratification.

Digital-Signature Oriented Steganography Approach against Man-in-the-Middle Attack

Nowadays, man-in-the-middle (MITM) attacks have become a large problem due to the advancement of computational power and interactive mobile technologies. Message security is a crucial concern that ought to be managed in order to help protect vital data from unauthorized people, such as MITM. Steganography is the technique of hiding secret data within an ordinary, non-secret file or message in order to avoid detection when communicating through an unsecured network. Steganography applications play a vital role in various fields that involve classified data transfer, such as healthcare, multimedia, and the military. Hence, the application of steganography in those fields attracts MITM attacks. Thus, in an attempt to address the challenge. This study proposes a hybrid approach that integrates an image steganography technique, the Advanced Encryption Standard (AES) for message encryption, and EdDSA (Edward-Curve Digital Signature Algorithm) for signature verification to enhance steganography against MITM attacks. The proposed hybrid approach was tested and measured using image metrics (MSE, PSNR, and SSIM) and histogram visualization and verified through experimentation. The results have proven that the proposed hybrid approach is an enhanced security approach with low execution time, more payload size for hiding messages, and a high invisibility embedded message to MITM compared with other existing approaches. This study has potential limitations. It does not explore tamper resistance or algorithm robustness, and it was not tested on a public image dataset.

Underwater image enhancement using contrast correction

AbstractLight‐induced degeneration of underwater images occurs by physical features of seawater. According to the wavelength of the colour spectrum, light reduces intensity significantly when it moves through water. The greatest wavelength of light that is visible gets absorbed first. Red and blue absorb the most and least, respectively. Because of the reducing consequences of the light spectrum, underwater images having poor contrast can be obtained. As a result, the crucial data contained inside these images will not be effectively retrieved for later analysis. The recent research suggests a novel approach to enhance the contrast while decreasing noise in underwater images. The recommended approach involves image histogram transformation using two significant colour spaces, Red‐Green‐Blue (RGB) and Hue‐Saturation‐Value (HSV). The histogram of the dominant colour channel (blue channel) in the RGB colour model is extended towards the lower level, containing a maximum limitation of 95%, while the inferior red colour channel has been extended towards the upper side, containing a minimum limitation of 5%. During the entire dynamic range, the green colour channel having the dominant and inferior colour channels expands in each direction. The Rayleigh distribution has been utilized for developing various stretching actions within the RGB colour space. The image has been converted to the HSV colour space, having the S and V elements adjusted within 1% of their minimum and maximum values. The suggested approach is examined in both qualitative and quantitative analysis. According to qualitative analysis, the recommended approach substantially boosts image contrast, lowers its blue and green effect, and minimizes over‐enhanced and under‐enhanced sections in the final resultant underwater image. The quantitative examination of 500 large scale underwater images dataset reveals that the suggested technique generates better results. The dataset images are grouped into small fish images, blue coral images, stone wall images, and coral branch images. The quantitative examination of all these four groups have been evaluated and shown. The average mean square error, peak signal to noise ratio, underwater image quality measurement, and underwater colour image quality evaluation values of dataset images are 76.69, 31.25, 3.85, and 0.64, respectively. These values of our proposed work outperform six other previous methods.

Image encryption algorithm based on DNA mutation and a novel four-dimensional hyperchaos

Abstract Aiming at the problem that insufficient complexity of ordinary multi-dimensional chaotic systems and the cumbersome design of encryption algorithms without excellent encryption effects. This paper constructs a four-dimensional hyperchaotic system with high Lyapunov exponent and complex dynamic behavior. We designed an encryption algorithm based on point mutation, mutation diffusion, and folding mutation in DNA mutations. During the encryption process, we perform point mutation transformation on the entire base sequence, then spread the mutations one by one starting from the second base of the sequence, and finally flip every four base sequences according to folding mutations. The images encrypted by this algorithm have a uniform grayscale histogram, high information entropy, and high key sensitivity. It can resist exhaustive attacks, noise attacks, cropping attacks, and differential attacks, and have a fast encryption speed.&amp;#xD;

Feature-Based Classification of Mild Cognitive Impairment and Alzheimer's Disease Based on Optical Coherence Tomographic Angiographic Image.

Alzheimer's disease is a type of neurodegenerative disorder that is characterized by the progressive degeneration of brain cells, leading to cognitive decline and memory loss. It is the most common cause of dementia and affects millions of people worldwide. While there is currently no cure for Alzheimer's disease, early detection and treatment can help to slow the progression of symptoms and improve quality of life. This research presents a diagnostic tool for classifying mild cognitive impairment and Alzheimer's diseases using feature-based machine learning applied to optical coherence tomographic angiography images (OCT-A). Several features are extracted from the OCT-A image, including vessel density in five sectors, the area of the foveal avascular zone, retinal thickness, and novel features based on the histogram of the range-filtered OCT-A image. To ensure effectiveness for a diverse population, a large local database for our study was collected. The promising results of our study, with the best accuracy of 92.17,% will provide an efficient diagnostic tool for early detection of Alzheimer's disease.