Automatic Computer-aided Diagnosis System Research Articles

Deep Learning-Based Computer-Aided Diagnosis (CAD): Applications for Medical Image Datasets.

Computer-aided diagnosis (CAD) has proved to be an effective and accurate method for diagnostic prediction over the years. This article focuses on the development of an automated CAD system with the intent to perform diagnosis as accurately as possible. Deep learning methods have been able to produce impressive results on medical image datasets. This study employs deep learning methods in conjunction with meta-heuristic algorithms and supervised machine-learning algorithms to perform an accurate diagnosis. Pre-trained convolutional neural networks (CNNs) or auto-encoder are used for feature extraction, whereas feature selection is performed using an ant colony optimization (ACO) algorithm. Ant colony optimization helps to search for the best optimal features while reducing the amount of data. Lastly, diagnosis prediction (classification) is achieved using learnable classifiers. The novel framework for the extraction and selection of features is based on deep learning, auto-encoder, and ACO. The performance of the proposed approach is evaluated using two medical image datasets: chest X-ray (CXR) and magnetic resonance imaging (MRI) for the prediction of the existence of COVID-19 and brain tumors. Accuracy is used as the main measure to compare the performance of the proposed approach with existing state-of-the-art methods. The proposed system achieves an average accuracy of 99.61% and 99.18%, outperforming all other methods in diagnosing the presence of COVID-19 and brain tumors, respectively. Based on the achieved results, it can be claimed that physicians or radiologists can confidently utilize the proposed approach for diagnosing COVID-19 patients and patients with specific brain tumors.

Developing an Artificial Intelligence Model for Reading Chest X-rays: Protocol for a Prospective Validation Study.

Chest x-rays are the most commonly used type of x-rays today, accounting for up to 26% of all radiographic tests performed. However, chest radiography is a complex imaging modality to interpret. Several studies have reported discrepancies in chest x-ray interpretations among emergency physicians and radiologists. It is of vital importance to be able to offer a fast and reliable diagnosis for this kind of x-ray, using artificial intelligence (AI) to support the clinician. Oxipit has developed an AI algorithm for reading chest x-rays, available through a web platform called ChestEye. This platform is an automatic computer-aided diagnosis system where a reading of the inserted chest x-ray is performed, and an automatic report is returned with a capacity to detect 75 pathologies, covering 90% of diagnoses. The overall objective of the study is to perform validation with prospective data of the ChestEye algorithm as a diagnostic aid. We wish to validate the algorithm for a single pathology and multiple pathologies by evaluating the accuracy, sensitivity, and specificity of the algorithm. A prospective validation study will be carried out to compare the diagnosis of the reference radiologists for the users attending the primary care center in the Osona region (Spain), with the diagnosis of the ChestEye AI algorithm. Anonymized chest x-ray images will be acquired and fed into the AI algorithm interface, which will return an automatic report. A radiologist will evaluate the same chest x-ray, and both assessments will be compared to calculate the precision, sensitivity, specificity, and accuracy of the AI algorithm. Results will be represented globally and individually for each pathology using a confusion matrix and the One-vs-All methodology. Patient recruitment was conducted from February 7, 2022, and it is expected that data can be obtained in 5 to 6 months. In June 2022, more than 450 x-rays have been collected, so it is expected that 600 samples will be gathered in July 2022. We hope to obtain sufficient evidence to demonstrate that the use of AI in the reading of chest x-rays can be a good tool for diagnostic support. However, there is a decreasing number of radiology professionals and, therefore, it is necessary to develop and validate tools to support professionals who have to interpret these tests. If the results of the validation of the model are satisfactory, it could be implemented as a support tool and allow an increase in the accuracy and speed of diagnosis, patient safety, and agility in the primary care system, while reducing the cost of unnecessary tests. PRR1-10.2196/39536.

Cardiac Anomaly Detection from Cine MRI Images using Physiological Features and Random Forest Classifier.

Computer-aided diagnosis (CAD) with cine MRI is a foremost research topic to enable improved, faster, and more accurate diagnosis of cardiovascular diseases (CVD). However, current approaches that use manual visualization or conventional clinical indices can lack accuracy for borderline cases. Also, manual visualization of 3D/4D MR data is time-consuming and expert-dependent. We try to simplify this process by creating an end-to-end automated CAD system that segments the critical substructures of the heart. The new domain-related physiological features are then calculated from the segmented regions. These features are fed to a random forest classifier that identifies the anomaly. We have obtained a very high accuracy when testing this end-to-end approach on the Automated Cardiac Diagnosis challenge (ACDC) dataset (4 pathologies, 1 normal). To prove the generalizability of the method we have blind-tested this approach on M&Ms-2 dataset which is a multi-center, multi-vendor, and multi-disease dataset with better than 90% accuracy.

Fractal Dimension Analysis of Melanocytic Nevi and Melanomas in Normal and Polarized Light-A Preliminary Report.

Clinical diagnosis of pigmented lesions can be a challenge in everyday practice. Benign and dysplastic nevi and melanomas may have similar clinical presentations, but completely different prognoses. Fractal dimensions of shape and texture can describe the complexity of the pigmented lesion structure. This study aims to apply fractal dimension analysis to differentiate melanomas, dysplastic nevi, and benign nevi in polarized and non-polarized light. A total of 87 Eighty-four patients with 97 lesions were included in this study. All examined lesions were photographed under polarized and non-polarized light, surgically removed, and examined by a histopathologist to establish the correct diagnosis. The obtained images were then processed and analyzed. Area, perimeter, and fractal dimensions of shape and texture were calculated for all the lesions under polarized and non-polarized light. The fractal dimension of shape in polarized light enables differentiating melanomas, dysplastic nevi, and benign nevi. It also makes it possible to distinguish melanomas from benign and dysplastic nevi under non-polarized light. The fractal dimension of texture allows distinguishing melanomas from benign and dysplastic nevi under polarized light. All examined parameters of shape and texture can be used for developing an automatic computer-aided diagnosis system. Polarized light is superior to non-polarized light for imaging texture details.

CVM-Cervix: A hybrid cervical Pap-smear image classification framework using CNN, visual transformer and multilayer perceptron

Cervical cancer is the seventh most common cancer among all the cancers worldwide and the fourth most common cancer among women. Cervical cytopathology image classification is an important method to diagnose cervical cancer. However, manual inspection is very troublesome, and experts are prone to make mistakes. The emergence of the automatic computer-aided diagnosis system solves this problem. This paper proposes a framework called CVM-Cervix based on deep learning to perform cervical cell classification tasks. It can analyze pap slides quickly and accurately. CVM-Cervix first proposes a Convolutional Neural Network module and a Visual Transformer module for local and global feature extraction respectively, then a Multilayer Perceptron module is designed to fuse the local and global features for the final classification. Experimental results show the effectiveness and potential of the proposed CVM-Cervix in the field of cervical Pap smear image classification. In addition, according to the practical needs of clinical work, we perform a lightweight post-processing to compress the model.

Design of automated computer aided diagnosis system to predict diabetic retinopathy based on EfficientNet

Design of automated computer aided diagnosis system to predict diabetic retinopathy based on EfficientNet

An attention-based weakly supervised framework for spitzoid melanocytic lesion diagnosis in whole slide images

Melanoma is an aggressive neoplasm responsible for the majority of deaths from skin cancer. Specifically, spitzoid melanocytic tumors are one of the most challenging melanocytic lesions due to their ambiguous morphological features. The gold standard for its diagnosis and prognosis is the analysis of skin biopsies. In this process, dermatopathologists visualize skin histology slides under a microscope, in a highly time-consuming and subjective task. In the last years, computer-aided diagnosis (CAD) systems have emerged as a promising tool that could support pathologists in daily clinical practice. Nevertheless, no automatic CAD systems have yet been proposed for the analysis of spitzoid lesions. Regarding common melanoma, no system allows both the selection of the tumor region and the prediction of the benign or malignant form in the diagnosis. Motivated by this, we propose a novel end-to-end weakly supervised deep learning model, based on inductive transfer learning with an improved convolutional neural network (CNN) to refine the embedding features of the latent space. The framework is composed of a source model in charge of finding the tumor patch-level patterns, and a target model focuses on the specific diagnosis of a biopsy. The latter retrains the backbone of the source model through a multiple instance learning workflow to obtain the biopsy-level scoring. To evaluate the performance of the proposed methods, we performed extensive experiments on a private skin database with spitzoid lesions. Test results achieved an accuracy of 0.9231 and 0.80 for the source and the target models, respectively. In addition, the heat map findings are directly in line with the clinicians' medical decision and even highlight, in some cases, patterns of interest that were overlooked by the pathologist.

A large margin piecewise linear classifier with fusion of deep features in the diagnosis of COVID-19

The world has experienced epidemics of coronavirus infections several times over the last two decades. Recent studies have shown that using medical imaging techniques can be useful in developing an automatic computer-aided diagnosis system to detect pandemic diseases with high accuracy at an early stage. In this study, a large margin piecewise linear classifier was developed to diagnose COVID-19 compared to a wide range of viral pneumonia, including SARS and MERS, using chest x-ray images. In the proposed method, a preprocessing pipeline was employed. Moreover, deep pre- and post-rectified linear unit (ReLU) features were extracted using the well-known VGG-Net19, which was fine-tuned to optimize transfer learning. Afterward, the canonical correlation analysis was performed for feature fusion, and fused deep features were passed into the LMPL classifier. The introduced method reached the highest performance in comparison with related state-of-the-art methods for two different schemes (normal, COVID-19, and typical viral pneumonia) and (COVID-19, SARS, and MERS pneumonia) with 99.39% and 98.86% classification accuracy, respectively.

An automatic Computer-Aided Diagnosis system based on the Multimodal fusion of Breast Cancer (MF-CAD)

The risk of death incurred by breast cancer is rising exponentially, especially among women. The early breast cancer diagnosis before it metastasizes helps medical staff controlling this disease, which decreases the risk of death. This made early breast cancer detection a crucial problem. Different imaging modalities offer complementary information concerning the same lesion helps to increase the performance of thcy fusing several modalities. This paper proposes a computerized features classification of breast cancer lesions through both the Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) and Digital Mammographic images (MGs). This study aims to investigate a Multimodal Fusion-based Computer-Aided Diagnosis (CAD) system, called MF-CAD, based on multivariate analysis of different modalities, for breast cancer mass detection. In this paper, firstly a new local feature descriptor is proposed in feature extraction, namely, the Gradient Local Information Pattern (GLIP), where we consider the gradient magnitude and orientation as well as the local differences as local binary features for DCE-MRI (or MGs) modality. Secondly, the fusion scheme is conducted using the Canonical Correlation Analysis (CCA) to highlight the intrinsic relation between these modalities. Finally, for comparative purposes, several selected machine learning classifiers (K-Nearest Neighbors, Support Vector Machine, Random forests, Artificial Neural Networks and Radial Basis Function Neural Network (RBFNN)) are used to distinguish between mass and No-mass breast images.Evaluation experiments of the diagnostic performances of our MF-CAD system are conducted over private datasets that contain both MG and DCE-MRI images acquired from 286 patients, which are “Breast DCE-MRI”, “Breast-MG” and “Breast Multimodal” datasets. Experimental results of the proposed MF-CAD system achieved an Area Under the ROC Curve (AUC) value of 99.10% using RBFNN classifier, while for each single modality alone, the best AUC values of 97.20% and 93.50% are obtained respectively for MG and DCE-MRI modalities using random forest classifier. A comparative study with recent existing approaches shows the competitive performances of the proposed approach.

Ensemble learning based automatic detection of tuberculosis in chest X-ray images using hybrid feature descriptors.

Tuberculosis (TB) remains one of the major health problems in modern times with a high mortality rate. While efforts are being made to make early diagnosis accessible and more reliable in high burden TB countries, digital chest radiography has become a popular source for this purpose. However, the screening process requires expert radiologists which may be a potential barrier in developing countries. A fully automatic computer-aided diagnosis system can reduce the need of trained personnel for early diagnosis of TB using chest X-ray images. In this paper, we have proposed a novel TB detection technique that combines hand-crafted features with deep features (convolutional neural network-based) through Ensemble Learning. Handcrafted features were extracted via Gabor Filter and deep features were extracted via pre-trained deep learning models. Two publicly available datasets namely (i) Montgomery and (ii) Shenzhen were used to evaluate the proposed system. The proposed methodology was validated with a k-fold cross-validation scheme. The area under receiver operating characteristics curves of 0.99 and 0.97 were achieved for Shenzhen and Montgomery datasets respectively which shows the superiority of the proposed scheme.

Automatic computer-aided diagnosis system for mass detection and classification in mammography

Mammography is currently the most powerful technique for early detection of breast cancer. To assist radiologists to better interpret mammogram images, computer-aided detection and diagnosis (CAD) systems have been proposed. This paper proposes a complete CAD system for mass detection and diagnosis, which consists of four steps. The first step consists of the preprocessing where the image is enhanced and the noise removed. In the second step, the abnormalities are segmented using the proposed HRAK algorithm. In the third step, the false positives are reduced using texture and shape features and the bagged trees classifier. Finally, the support vector machine (SVM) is used to classify the abnormalities as malignant or benign. The proposed CAD system is verified with both the MIAS and CBIS-DDSM databases. The experimental results proved to be successful. The accuracy detection rate achieves 93,15% for sensitivity and 0,467 FPPI for MIAS and 90,85% for sensitivity and 0,65 FPPI for CBIS-DDSM. The accuracy classification rate achieves 94,2% and the AUC 0,95 for MIAS and 90,44% and 0,9 for CBIS-DDSM.

An experimental study on classification of thyroid histopathology images using transfer learning

• An automated computer aided diagnosis system using deep learning is proposed. • The proposed system classifies thyroid histopathology images into two classes. • Transfer learning is applied to 5 different pre-trained convolutional neural networks. • Data augmentation is employed to improve the diversity of dataset. • Results show the success of transfer learning to classify thyroid histopathology images. CAD systems for histopathology image analysis using machine learning is a well researched subject. Deep learning is playing a major role in advancing this research in the recent years. This paper presents an automated thyroid histopathology image classification system with deep neural networks using the theory of transfer learning and popular pre-trained convolutional neural networks (CNNs). In this experiment-based study, two forms of transfer learning namely feature extraction and fine tuning are applied on popular state-of-the-art CNN architectures such as VGGNet, ResNet, InceptionNet and DenseNet to classify thyroid histopathology images. Accuracy, precision, sensitivity, specificity, area under receiver operating characteristic (AUROC) analysis and F1-score are used to evaluate the performance of the architectures. The results are promising and demonstrate the feasibility of transfer learning for thyroid histopathology image analysis.

Automatic Grading of Retinal Blood Vessel in Deep Retinal Image Diagnosis

Automatic grading of retinal blood vessels from fundus image can be a useful tool for diagnosis, planning and treatment of eye. Automatic diagnosis of retinal images for early detection of glaucoma, stroke, and blindness is emerging in intelligent health care system. The method primarily depends on various abnormal signs, such as area of hard exudates, area of blood vessels, bifurcation points, texture, and entropies. The development of an automated screening system based on vessel width, tortuosity, and vessel branching are also used for grading. However, the automated method that directly can come to a decision by taking the fundus images got less attention. Detecting eye problems based on the tortuosity of the vessel from fundus images is a complicated task for opthalmologists. So automated grading algorithm using deep learning can be most valuable for grading retinal health. The aim of this work is to develop an automatic computer aided diagnosis system to solve the problem. This work approaches to achieve an automatic grading method that is opted using Convolutional Neural Network (CNN) model. In this work we have studied the state-of-the-art machine learning algorithms and proposed an attention network which can grade retinal images. The proposed method is validated on a public dataset EIARG1, which is only publicly available dataset for such task as per our knowledge.

Automatic segmentation and classification of liver tumor from CT image using feature difference and SVM based classifier-soft computing technique

The liver is essential for endurance and to carry out a large number of significant functions, including manufacture of indispensable proteins, and metabolism of fats and carbohydrates. The examination of CT might be employed for planning and managing the treatments for tumor in a proper way and for directing biopsies as well as other simply determined process. The Manual segmentation and Computed Axial Tomography (CT) image classification is a tedious task and time consuming process for large amount of data. Computer-Aided Diagnosis (CAD) systems take part in a fundamental role in the detection of liver disease in an early stage and therefore decrease death rate of liver cancer. In this paper an automatic CAD system is presented in three stage. In the first step, automatic liver segmentation and lesion’s detection is carried out. Then, the next step is to extract features. At last, liver lesions classification into malignant and benign is done by using the novel contrast based feature-difference method. The extracted features from the lesion area with its surrounding normal liver tissue are based on intensity and texture. The lesion descriptor is obtained by considering the difference between the features of both lesion area and normal tissue of liver. Finally to categorize the liver lesions into malignant or benign a new SVM based machine learning classifier is trained on the new descriptors. The investigational outcome show hopeful improvement. Besides, the projected approach is insensitive to ranges of textures and intensity between demographics, imaging devices, and patients and settings. The classifier discriminates the tumor by comparatively high precision and offers a subsequent view to the radiologist.

540 Assessment of left ventricular ejection fraction from echocardiographic images using machine learning algorithm

Abstract Background Precise and reliable echocardiographic assessment of LVEF is needed for clinical decision-making. LVEF is currently determined through an observer dependent process that requires manual tracing. To remove this manual tracing step, which is both time-consuming and user dependent, automatic computer aided diagnosis systems may be useful in the clinical setting. Purpose The aim of this study was to evaluate whether a 3-dimensional convolutional neural networks (3DCNN) could estimate left ventricular ejection fraction (LVEF) and differentiate types of heart failure (preserved EF/reduced EF) using conventional 2-dimensional echocardiographic images. Methods We developed a deep learning model to automatically estimate LVEF from echocardiographic data. The 3DCNN model was trained on a dataset of 340 patients. The dataset creation consisted of three main steps: firstly, for each exam, cine-loops showing the parasternal and apical views were manually selected; then, 10 sequential frames were extracted from each 1 beat and; finally, each frame was pre-processed to fit the learning model. Each patient has 2 views, resulting in a total of 6,800 images. Reference LVEF measurement was calculated by two highly experienced readers in each case. Results A good correlation was found between estimated LVEF based on apical 2 and 4 chamber views and reference LVEF (r =0.88, p <0.001) (Figure). For classification of heart failure types based on LVEF (LVEF ≥50% or <50%), the area under the receiver-operating characteristic curve by the 3DCNN algorithm was over 0.95. Conclusions The 3DCNN can be applied to estimate and classify the LVEF in the clinical setting. Furthermore, this work will serve as a driver for future research using million image databases. Abstract 540 Figure.

Deep Convolutional Neural Network-Based Automatic Classification of Neonatal Hip Ultrasound Images: A Novel Data Augmentation Approach with Speckle Noise Reduction

Neonatal hip ultrasound imaging has been widely used for a few decades in the diagnosis of developmental dysplasia of the hip (DDH). Graf's method of hip ultrasonography is still the most reproducible because of its classification system; yet, the reproducibility is questionable as a result of the high dependency on the skills of the sonographer and the evaluator. A computer-aided diagnosis system may help evaluators increase their precision in the diagnosis of DDH using Graf's method. This study describes a fully automatic computer-aided diagnosis system for the classification of hip ultrasound images captured in Graf's standard plane based on convolutional neural networks (CNNs). Automatically segmented image patches containing all of the necessary anatomical structures were given to the proposed CNN system to extract discriminative features and classify the recorded hips. For ease of evaluation, the data set was divided into three groups: normal, mild dysplasia and severe dysplasia. This study proposes a different approach to data augmentation using speckle noise reduction with an optimized Bayesian non-local mean filter. Data augmentation with this filter increased the accuracy of the proposed CNN system from 92.29% to 97.70%. This new approach for automatic classification of DDH, classifies dysplastic neonatal hips with a high accuracy rate and might help evaluators to increase their evaluation success.

Artery/Vein Vessel Tree Identification in Near-Infrared Reflectance Retinographies.

An accurate identification of the retinal arteries and veins is a relevant issue in the development of automatic computer-aided diagnosis systems that facilitate the analysis of different relevant diseases that affect the vascular system as diabetes or hypertension, among others. The proposed method offers a complete analysis of the retinal vascular tree structure by its identification and posterior classification into arteries and veins using optical coherence tomography (OCT) scans. These scans include the near-infrared reflectance retinography images, the ones we used in this work, in combination with the corresponding histological sections. The method, firstly, segments the vessel tree and identifies its characteristic points. Then, Global Intensity-Based Features (GIBS) are used to measure the differences in the intensity profiles between arteries and veins. A k-means clustering classifier employs these features to evaluate the potential of artery/vein identification of the proposed method. Finally, a post-processing stage is applied to correct misclassifications using context information and maximize the performance of the classification process. The methodology was validated using an OCT image dataset retrieved from 46 different patients, where 2,392 vessel segments and 97,294 vessel points were manually labeled by an expert clinician. The method achieved satisfactory results, reaching a best accuracy of 93.35% in the identification of arteries and veins, being the first proposal that faces this issue in this image modality.

A fully automatic computer-aided diagnosis system for hepatocellular carcinoma using convolutional neural networks

The cancer of liver, which is the leading cause of cancer death, is commonly diagnosed by comparing the changes of gray level of liver tissue in the different phases of the patient's CT images. To aid the doctor in reducing misdiagnosis or missed diagnosis, a fully automatic computer-aided diagnosis (CAD) system is proposed to diagnose hepatocellular carcinoma (HCC) using convolutional neural network (CNN) classifier. The automatic segmentation and classification are two core technologies of the proposed CAD system, which are both realized based on CNN. The segmentation of liver and tumor is implemented by a fully convolutional networks (FCN) based on a fine tuning VGG-16 model with two additional ‘skip structures’ using a weighted loss function which helps to solve the problem of inaccurate tumor segmentation caused by the inevitably unbalanced training data. HCC classification is implemented by a 9-layer CNN classifier, whose input is a 4-channel image data constructed by combining the segmentation result of FCN with the original CT image. A total of 165 venous phase CT images including 46 diffuse tumors, 43 nodular tumors, and 76 massive tumors are used to evaluate the performance of the proposed CAD system. The classification accuracy of CNN classifier for diffuse, nodular and massive tumors are 98.4%, 99.7% and 98.7% respectively, which are significantly improved in contrast with the traditional feature-based ANN and SVM classifiers. The proposed CAD system, which is unaffected by the difference of preprocessing method and feature type, is proved satisfactory and feasible by the test set.

Adaptive color deconvolution for histological WSI normalization

Background and ObjectiveColor consistency of histological images is significant for developing reliable computer-aided diagnosis (CAD) systems. However, the color appearance of digital histological images varies across different specimen preparations, staining, and scanning situations. This variability affects the diagnosis and decreases the accuracy of CAD approaches. It is important and challenging to develop effective color normalization methods for digital histological images. MethodsWe proposed a novel adaptive color deconvolution (ACD) algorithm for stain separation and color normalization of hematoxylin-eosin-stained whole slide images (WSIs). To avoid artifacts and reduce the failure rate of normalization, multiple prior knowledges of staining are considered and embedded in the ACD model. To improve the capacity of color normalization for various WSIs, an integrated optimization is designed to simultaneously estimate the parameters of the stain separation and color normalization. The solving of ACD model and application of the proposed method involves only pixel-wise operation, which makes it very efficient and applicable to WSIs. ResultsThe proposed method was evaluated on four WSI-datasets including breast, lung and cervix cancers and was compared with 6 state-of-the-art methods. The proposed method achieved the most consistent performance in color normalization according to the quantitative metrics. Through a qualitative assessment for 500 WSIs, the failure rate of normalization was 0.4% and the structure and color artifacts were effectively avoided. Applied to CAD methods, the area under receiver operating characteristic curve for cancer image classification was improved from 0.842 to 0.914. The average time of solving the ACD model is 2.97 s. ConclusionsThe proposed ACD model has prone effective for color normalization of hematoxylin-eosin-stained WSIs in various color appearances. The model is robust and can be applied to WSIs containing different lesions. The proposed model can be efficiently solved and is effective to improve the performance of cancer image recognition, which is adequate for developing automatic CAD programs and systems based on WSIs.

Exploration of Multiscale Geometric Analysis for Enhancement and Structure Detection in MRCP Images

Magnetic Resonance Cholangiopancreatography (MRCP) images are used in the diagnosis and treatment planning of the bile ducts and the liver. The ability to diagnose and treat such diseases in recent times has increased with greater importance placed on the effective use of non-invasive medical imaging. However, MRCP images are often noisy (low signal-to-noise ratio) and suffer from the influence of artifacts, partial volume and large inter-image variations. This paper evaluates the suitability of several Multiscale Geometric Analysis (MGA) techniques, to improve the quality of the 2D MRCP images and enhance the detection of the biliary structures within them. Tests results, evaluated subjectively to correspond with the human visual perception, achieved promising results for contourlets, curvelets as well as a non-orthogonal wavelet noise compensation implementation. The findings may contribute to assist in the development of automatic computer-aided diagnosis systems for liver diseases affecting the bile ducts as well as in clinical diagnosis.