Radiologists’ Preferences for Digital Mammographic Display

We have devised a mammogram modeling system which greatly simplifies the development of, and can improve the accuracy and consistency of, computer-aided display and analysis algorithms for digital mammography. Our system segments the five major components of a mammogram: background, uncompressed-fat, fat, dense, and muscle. Differences in the amount and distribution of these components account for much of the variation between mammograms. Via segmentation, the corresponding variations are isolated; automated algorithms can consider the components independently or adapt their parameters based on component-specific statistics.In this paper, we present our system and demonstrate its versatility. Our system is able to segment a wide variety of digital mammograms because of its combined use of geometric (i.e., gradient magnitude ridge traversal) and statistical (i.e., Gaussian mixture modeling) techniques. Using images from Fischer, General Electric, and Trex digital mammography units, we define and evaluate automated, component-based algorithms for (1) “general” intensity windowing, i.e., displaying a digital mammogram such that it resembles a screen-film mammogram for breast cancer screening; (2) component-specific intensity windowing for breast lesion characterization; and (3) breast density estimation for breast cancer risk assessment.KeywordsWater LoadWater ExcretionChronic Congestive Heart FailureDigital MammogramPeritoneovenous ShuntingThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Digital chest radiography offers many advantages over filmbased radiography, such as immediate image display, no film processing and room storage, wider dynamic range and lower radiation dose. In general, a raw X-ray image acquired directly from a digital flat detector contains poor quality of image, which may not be suitable for diagnosis and treatment planning. Therefore, a pre-processing technique is usually required to enhance image quality. This paper presents an improved image enhancement on digital chest radiography using the so-called N-CLAHE method, which is based on global and local enhancement. The proposed technique consists of two main steps. Firstly, intensity correction of the raw image is encountered by the log-normalization function which adjusts the intensity contrast of the image dynamically. Secondly, the Contrast Limited Adaptive Histogram Equalization (CLAHE) method is used for enhancing small details, textures and local contrast of the images. The proposed approach was tested using a radiographic survey phantom and a radiographic chest phantom and compared with conventional enhancement methods, such as histogram equalization, unsharp masking, CLAHE. The results show that the proposed N-CLAHE method yields great improvement on the pre-processing correction for digital chest radiography.

Digital image processing deals with the manipulation of digital images through a digital computer. It is a subfield of signals and systems but focuses particularly on images. DIP focuses on developing a computer system that can perform processing on an image. The input of that system is a digital image and the system process that image using efficient algorithms and gives an image as an output. The most common example is Adobe Photoshop. It is one of the widely used applications for processing digital images. The image processing techniques play a vital role in image Acquisition, image pre-processing, Clustering, Segmentation, and Classification techniques with different kinds of images such as Fruits, Medical, Vehicle, and Digital text images, etc. In this study, the various images remove unwanted noise and performance enhancement techniques such as contrast limited adaptive histogram equalization.

Introduction: A decrease in mammographic density is increasingly used as an intermediate marker to evaluate the effect of interventions for reducing breast cancer risk. Technical aspects, such as a different amount of compression, could influence density measurements. We investigated the inter-scan reproducibility of mammographic density measurements to see whether this may influence measurements of true changes in density. Methods: We included women with two mammograms made closely after one another (median 12 days), with no true changes in breast density expected to happen: 106 had two Full-Field Digital Mammograms (FFDM), 12 had two Film-Screen Mammograms (FSM) and 52 had one FFDM and one FSM. We estimated intraclass correlation coefficients, and mean absolute density differences between both mammograms. Results: The intraclass correlation between percent density measurements on two FFDMs was 0.93 (95%CI 0.91-0.95), between two FSMs 0.94 (95%CI 0.84-0.98), and between FFDM and FSM 0.72 (95%CI 0.61-0.80). The mean absolute difference in percent density between two FFDMs was 4.3% (95%CI 3.8-4.9) and between two FSMs 6.5% (95%CI 3.4-9.6). On average, percent density in the same woman was higher on FFDM than on FSM (difference 12.9% (95%CI 11.0-14.8)). Conclusion: When studying changes in mammographic density, it is important to compare two mammograms of the same type (FSM or FFDM). Even then, although intraclass correlation coefficients are high, the absolute differences in percent density are still of a magnitude that may influence detectability of true changes in mammographic density. Citation Format: Mariëtte Lokate, Marleen Emaus, Michiel Kallenberg, Marije Bakker, Nico Karssemeijer, Wouter Veldhuis, Petra Peeters, Carla van Gils. Inter-scan reproducibility of mammographic density measurements. [abstract]. In: Proceedings of the Eleventh Annual AACR International Conference on Frontiers in Cancer Prevention Research; 2012 Oct 16-19; Anaheim, CA. Philadelphia (PA): AACR; Cancer Prev Res 2012;5(11 Suppl):Abstract nr A116.

Accurate detection of egg fertility is crucial to improve hatching success in duck farming. Conventional candling methods rely heavily on human expertise, making them subjective and error-prone. This study proposes an automated classification system for Cihateup duck egg fertility using candling images and a convolutional neural network (CNN) based on the EfficientNet-B3 architecture. Image enhancement techniques, including contrast limited adaptive histogram equalization (CLAHE), unsharp masking, and adaptive thresholding, were applied to improve image quality and feature visibility. The dataset consisted of fertile and infertile egg images captured at two incubation stages: the first 24 hours and the 8th–15th days. Data were split into training, validation, and testing sets with a ratio of 70:15:15. Experimental results show that image enhancement significantly improves classification performance. Without enhancement, the model achieved an accuracy of 49% with an area under curve (AUC) of 0.4226, indicating poor discrimination capability. With image enhancement, the proposed method achieved accuracies of 77% for the first 24 hours dataset and 80% for the 8th–15th days dataset, with AUC values of 0.9962 and 0.9317, respectively. These results demonstrate that EfficientNet-B3 combined with image enhancement provides an effective and computationally efficient solution for automated fertility detection of Cihateup duck eggs.

In this paper we present a generalized algorithm for unsharp masking of medical images which takes as one of its inputs a high contrast image underwent local adaptive contrast enhancement. Selection of optimal values of the number of histogram bins, processing window size and intensity lower and upper limits in iterative manner is part of applying Contrast Limited Adaptive Histogram Equalization (CLAHE). Experimental results reveal higher quality of the output images both in terms of root mean square contrast and sharpness. Achieved quality, both visually and quantitatively, is compared to that from the Adaptive Histogram Equalization (AHE) algorithm, limited histogram stretching and ordinary histogram equalization which proves its applicability. The algorithm is considered appropriate for processing a number of types of images, such as CT, X-ray, etc.

Adaptive histogram equalization (AHE) is a method for adaptive contrast enhancement of digital images. It is an automatic, reproducible method for the simultaneous viewing of contrast within a digital image with a large dynamic range. Recent experiments have shown that in specific cases, there is no significant difference in the ability of AHE and linear intensity windowing to display gray-scale contrast. More recently, a variant of AHE which limits the allowed contrast enhancement of the image has been proposed. This contrast-limited adaptive histogram equalization (CLAHE) produces images in which the noise content of an image is not excessively enhanced, but in which sufficient contrast is provided for the visualization of structures within the image. Images processed with CLAHE have a more natural appearance and facilitate the comparison of different areas of an image. However, the reduced contrast enhancement of CLAHE may hinder the ability of an observer to detect the presence of some significant gray-scale contrast. In this report, a psychophysical observer experiment was performed to determine if there is a significant difference in the ability of AHE and CLAHE to depict gray-scale contrast. Observers were presented with computed tomography (CT) images of the chest processed with AHE and CLAHE. Subtle artificial lesions were introduced into some images. The observers were asked to rate their confidence regarding the presence of the lesions; this rating-scale data was analyzed using receiver operating characteristic (ROC) curve techniques. These ROC curves were compared for significant differences in the observers' performances. In this report, no difference was found in the abilities of AHE and CLAHE to depict contrast information.

In diagnosing breast cancer, digital mammograms have shown their effectiveness as an appropriate and simple instrument in the early detection of tumor. Mammograms offer helpful cancer symptoms information, including microcalcifications and masses, which are not easy to distinguish because there are some flaws with the mammography images, including low contrast, high noise, fuzzy and blur. Additionally, there is a major problem with mammography because of a high density of the breast which conceals As a result of the mammographic image, it is more difficult to distinguish between the tissues with normal dense and the tissues that are cancerous. Therefore, mammography images need to be improved in order to accurately identify and diagnose breast cancer. The most typical goals of images enhancement are to remove noise and improve image details. With the aid of mammography image processing techniques, a special data including distinctive characteristics of tumors can be differentiated, this could help distinguish between malignant and benign cancers. This work focuses on removing noise of pepper & salt, improving image to increase the quality of mammography and enhance early detection of breast cancer. A specific approach is employed to do this, including of two phases of image denoising base filtration and one phase to improve contrast. The stages of filtering contain the using of wiener and median filters. The contrast enhancement stage utilizes (CLAHE) which is an abbreviation for contrast limited adaptive histogram equalization. Evaluating the performance is done via contrast histogram for the CLAHE and MSE & PSNF for the filters. The results demonstrate that the work technique is doing better when put to comparison with other approaches in term of low MSE (1.1645) and high PSNR (47.4750). The technique will be assessed with additional kinds of noise for future work.

The identification of COVID-19 using chest X-ray (CXR) images plays a critical role in managing the pandemic by providing a rapid, non-invasive, and accessible diagnostic tool. This study evaluates the impact of different image preprocessing techniques on the performance of deep learning models for COVID-19 classification based on COVID-19 Radiography Database, which includes 10,192 normal CXR images, 6012 lung opacity (non-COVID lung infection) images, and 1345 viral pneumonia images. Along with the images, corresponding lung masks are also included to aid in the segmentation and analysis of lung regions. Specifically, three convolutional neural network (CNN) models were developed, each using a distinct preprocessing method: Contrast Limited Adaptive Histogram Equalization (CLAHE), traditional histogram equalization, and no preprocessing. The results revealed that while the CLAHE-enhanced model achieved the highest training accuracy (93.26%) and demonstrated superior stability during training, it showed lower performance in the validation phase, with validation accuracy of 91.31%. In contrast, the model with no preprocessing, which exhibited slightly lower training accuracy (92.98%), outperformed the CLAHE model during validation, achieving the highest validation accuracy of 91.50% and the lowest validation loss. The histogram equalization model demonstrated performance similar to that of CLAHE but with slightly higher validation loss and accuracy compared to the unprocessed model. These findings suggest that while CLAHE excels in enhancing image details during training, it may lead to overfitting and reduced generalization ability. In contrast, the model without preprocessing showed the best generalization and stability, indicating that preprocessing techniques should be chosen carefully to balance feature enhancement with the need for generalization in real-world applications. This study underscores the importance of selecting appropriate image preprocessing techniques to enhance deep learning models' performance in medical image classification, particularly for COVID-19 detection. Histogram Equalization The results contribute to ongoing efforts to optimize diagnostic tools using AI and image processing.

In this study, deep convolutional neural networks (CNNs) are applied in computer-aided diagnosis of three types of disc herniation disease based on lumbar Axial MR Images. AlexNet architecture is explored and evaluated in order to classify images into four groups: Normal, Bulge, Protrusion and Extrusion. First, a large-scale dataset of 2329 scanned MRI images is gathered from local medical centers, then it is extended by using Visual Rotation and Mirror Reversal techniques to 9316 images. The CNNs are used for feature extraction and feature reduction in convolution layers; in addition, classification is performed by the CNN SoftMax layer. Also, to improve MR images resolution, CLAHE (Contrast-Limited Adaptive Histogram Equalization) and USM (UnSharp Masking) filters, which are of critical importance to the increase of the final accuracy, are employed. A region of interest (ROI) is then selected to reduce the size of the input images and eliminate additional features causing overhead on network. A number of changes are made to original AlexNet model including adding a Batch Normalization layer, as well as initial Xavier initialization, with remarkable effectiveness displayed in results. Also, instead of fully-connected layers, 1×1 convolution layer is applied to test accuracy. Finally, the proposed method is compared with the results of three state-of-the-art methods. Experimental results prove that this deep CNN results enjoy an improvement compared to former proposed methods; the accuracy, sensitivity and specificity for random sub sampling method are 87.75%, 86.5% and 94.75%, respectively, achieved by AlexNet architecture of CNN.

Skin cancer is a class of disorder defined by the growth of abnormal cells on the body. Accurately identifying and diagnosing skin lesions is quite difficult because skin malignancies share many common characteristics and a wide range of morphologies. To face this challenge, deep learning algorithms have been proposed. Deep learning algorithms have shown diagnostic efficacy comparable to dermatologists in the discipline of images-based skin lesion diagnosis in recent research articles. This work proposes a novel deep learning algorithm to detect skin cancer. The proposed CAD-Skin system detects and classifies skin lesions using deep convolutional neural networks and autoencoders to improve the classification efficiency of skin cancer. The CAD-Skin system was designed and developed by the use of the modern preprocessing approach, which is a combination of multi-scale retinex, gamma correction, unsharp masking, and contrast-limited adaptive histogram equalization. In this work, we have implemented a data augmentation strategy to deal with unbalanced datasets. This step improves the model's resilience to different pigmented skin conditions and avoids overfitting. Additionally, a Quantum Support Vector Machine (QSVM) algorithm is integrated for final-stage classification. Our proposed CAD-Skin enhances category recognition for different skin disease severities, including actinic keratosis, malignant melanoma, and other skin cancers. The proposed system was tested using the PAD-UFES-20-Modified, ISIC-2018, and ISIC-2019 datasets. The system reached accuracy rates of 98%, 99%, and 99%, consecutively, which is higher than state-of-the-art work in the literature. The minimum accuracy achieved for certain skin disorder diseases reached 97.43%. Our research study demonstrates that the proposed CAD-Skin provides precise diagnosis and timely detection of skin abnormalities, diversifying options for doctors and enhancing patient satisfaction during medical practice.

This research addresses the lack of publicly available datasets for Bangladeshi medicinal plants by presenting a comprehensive dataset comprising 5000 images of ten species collected under controlled conditions. To improve performance, several preprocessing techniques were employed, such as image selection, background removal, unsharp masking, contrast-limited adaptive histogram equalization, and morphological gradient. Then, we applied five state-of-the-art deep learning models to achieve benchmark performance on the dataset: VGG16, ResNet50, DenseNet201, InceptionV3, and Xception. Among these models, DenseNet201 demonstrated the highest accuracy of 85.28%. In addition to benchmarking the deep learning models, three novel neural network architectures were developed: dense-residual–dense (DRD), dense-residual–ConvLSTM-dense (DRCD), and inception-residual–ConvLSTM-dense (IRCD). The DRCD model achieved the highest accuracy of 97%, surpassing the benchmark performances of individual models. This highlights the effectiveness of the proposed architectures in capturing complex patterns and dependencies within the data. To further enhance classification accuracy, an ensemble approach was adopted, employing both hard ensemble and soft ensemble techniques. The hard ensemble achieved an accuracy of 98%, while the soft ensemble achieved the highest accuracy of 99%. These results demonstrate the effectiveness of ensembling techniques in boosting overall classification performance. The outcomes of this study have significant implications for the accurate identification and classification of Bangladeshi medicinal plants. This research provides valuable resources for traditional medicine, drug discovery, and biodiversity conservation efforts. The developed models and ensemble techniques can aid researchers, botanists, and practitioners in accurately identifying medicinal plant species, thereby facilitating the utilization of their therapeutic potential and contributing to the preservation of biodiversity.

Technique for preprocessing of digital mammogram

In the contemporary world, breast tumor is the most consistently spotted neoplasm among women, that is, instigating women’s transience at a higher rate. Radiologists prefer computer-aided mammography in order to detect breast cancer. Mammograms consist of many artifacts like labels, pectoral muscles whose removal is a daunting task. The presence of glandular tissues in digital mammograms plays a pivotal role in the early finding of breast cancer. This is necessary to find the radiation risk linked with screening and also to find the irregularity between right and left breasts. The proposed method focuses on pre-processing and breast density segmentation. In this method, both binarization and the modified region growing method are applied for removing the labels, background and pectoral muscles. For improving the contrast of the images bereft of the pectoral muscles, it uses the contrast limited adaptive histogram equalization with Rayleigh distribution. Next use is made of K-means clustering to segment the digital mammograms into different density regions. The proposed method is applied on a publicly accessible mini-MIAS database. Its results are validated by the expert radiologists and these compete with those of the state of the art methods.KeywordsBreast cancerMammogramsPectoral musclesImage segmentationBinarizationRegion growingmini-MIAS databaseK-means clusteringContrast limited adaptive histogram equalization

In the rapidly evolving landscape of agricultural technology, image processing has emerged as a powerful tool for addressing critical agricultural challenges, with a particular focus on the identification and management of crop diseases. This study is motivated by the imperative need to enhance agricultural sustainability and productivity through precise plant health monitoring. Our primary objective is to propose an innovative approach combining support vector machine (SVM) with advanced image processing techniques to achieve precise detection and classification of fig leaf diseases. Our methodology encompasses a step-by-step process, beginning with the acquisition of digital color images of diseased leaves, followed by denoising using the mean function and enhancement through Contrast-limited adaptive histogram equalization. The subsequent stages involve segmentation through the Fuzzy C Means algorithm, feature extraction via Principal Component Analysis, and disease classification, employing Particle Swarm Optimization (PSO) in conjunction with SVM, Backpropagation Neural Network, and Random Forest algorithms. The results of our study showcase the exceptional performance of the PSO SVM algorithm in accurately classifying and detecting fig leaf disease, demonstrating its potential for practical implementation in agriculture. This innovative approach not only underscores the significance of advanced image processing techniques but also highlights their substantial contributions to sustainable agriculture and plant disease mitigation. In conclusion, the integration of image processing and SVM-based classification offers a promising avenue for advancing crop disease management, ultimately bolstering agricultural productivity and global food security.