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Controlled despeckling (structure/edges/feature preservation with smoothing the homogeneous areas) is a desired pre-processing step for the design of computer-aided diagnostic (CAD) systems using ultrasound images as the presence of speckle noise masks diagnostically important information making interpretation difficult even for experienced radiologist. For efficiently classifying the breast tumors, the conventional CAD system designs use hand-crafted features. However, these features are not robust to the variations in size, shape and orientation of the tumors resulting in lower sensitivity. Thus deep feature extraction and classification of breast ultrasound images have recently gained attention from research community. The deep networks come with an advantage of directly learning the representative features from the images. However, these networks are difficult to train from scratch if the representative training data is small in size. Therefore transfer learning approach for deep feature extraction and classification of medical images has been widely used. In the present work the performance of four pre-trained convolutional neural networks VGG-19, SqueezeNet, ResNet-18 and GoogLeNet has been evaluated for differentiating between benign and malignant tumor types. From the results of the experiments, it is noted that CAD system design using GoogLeNet architecture for deep feature extraction followed by correlation based feature selection and fuzzy feature selection using ANFC-LH yields highest accuracy of 98.0% with individual class accuracy value of 100% and 96% for benign and malignant classes respectively. For differentiating between the breast tumors, the proposed CAD system design can be utilized in routine clinical environment.

The speckle noise is an intrinsic artefact present in ultrasound images that masks the diagnostically important information, thus makes it hard for the radiologists to analyze them. Therefore, a suitable despeckling algorithm, which will retain the diagnostically important features such as structure, edges and margins are required. In this study the performance of 64 despeckling filters algorithms used for the analysis of thyroid nodule ultrasound images is compared. These 64 filters are divided into 9 categories namely Linear, Non-linear, Total Variation, Fuzzy, Fourier, Multiscale, Nonlocal Mean, Edge Preserving, and Hybrid filters. A total of 820 thyroid US images have been taken from two different benchmark datasets. Out of these 820 thyroid US images, 200 are benign and 620 are malignant. The performance analysis of despeckling filters has been carried out by calculating structure and edge preservation index metric. It has been observed that fast bilateral filter and edge-preserving smoothing filter yields optimal performance with respect to the preservation of image structures like edges and margins of benign and malignant thyroid tumors. Based on the criterion followed in real time clinical practice for differential diagnosis between benign and malignant thyroid ultrasound tumors, it is observed that the images filtered by DsF_EPSF filter yields better diagnostic quality images in terms of preservation and enhancement of important diagnostic information.

In current consequence of haematology, blood cancer i.e. acute lymphoblastic leukemia is very frequently founded in medical practice, which is characterized by over activation and functional abnormality of bone marrow. The abnormality is identified through physical examination with a screening of blood smears. However, this method is error prone and labor intensive task for haematologist. Hence, haematologist needs a specific computer aided diagnostic system (CAD) that can deal with these limitations of prior systems and capable of discriminating immature leukemic cells from mature healthy cells. Thus, this work addresses the problem of segmenting a microscopic blood image into different regions, and then further analyzes those regions for localization of the immature lymphoblast cell. Further, it investigates the use of different geometrical, chromatic and statistical textures features for nucleus as well as cytoplasm and pattern recognition techniques for sub typing immature acute lymphoblasts as per FAB (French– American – British) classification. This can facilitate haematologist for acquiring essential information about prognosis and for an appropriate cure for leukemia. The exhaustive experiments have been conducted on 260 microscopic blood images (i.e. 130 normal and 130 cancerous cells) taken from ALL-IDB database. The proposed techniques consisting of the segmentation module used for segmenting the nucleus and cytoplasm of each leukocyte cell, feature extraction module, feature dimensionality reduction module that uses principal component analysis (PCA) to mapped the higher feature space to lower feature space and classification module that employs the standard classifiers, like support vector machines, smooth support vector machines, k-nearest neighbour, probabilistic neural network and adaptive neuro fuzzy inference system.

In the present work, a hybrid hierarchical framework for classification of breast density using digitized film screen mammograms has been proposed. For designing of an efficient classification framework 480 MLO view digitized screen film mammographic images are taken from DDSM dataset. The ROIs of fixed size i.e. 128 × 128 pixels are cropped from the center area of the breast (i.e. the area where glandular ducts are prominent). A total of 292 texture features based on statistical methods, signal processing based methods and transform domain based methods are computed for each ROI. The computed feature vector is subjected to PCA for dimensionality reduction. The reduced feature space is fed to the classification module. In this work 4-class breast density classification has been conducted using hierarchical framework where the first classifier is used to classify an unknown test ROI into B-I / other class . If the test ROI is predicted as other class , it is inputted to second classifier for the classification into B-II / dense class . If the test ROI is predicted as belonging to dense class , it is inputted to classifier for the classification into B-III / B-IV class. In this work five hierarchical classifiers designs consisting of 3 PCA- k NN, 3 PCA-PNN, 3 PCA-ANN, 3 PCA-NFC and 3 PCA-SVM classifiers has been proposed. The obtained maximum OCA value is 80.4% using PCA-NFC in hierarchical approach. Further, the best performing individual classifiers are clubbed together in a hierarchical framework to design hybrid hierarchical framework for classification of breast density using digitized screen film mammograms. The proposed hybrid hierarchical framework yields the OCA value of 84.1%. The result achieved by the proposed hybrid hierarchical framework is quite promising and can be used in clinical environment for differentiation between different breast density patterns.

Computer-Aided Diagnosis (CAD) system is preferred for automatic thyroid tumor ultrasound image characterization instead of manual assessment by the experts. Segmentation and control despeckling are the important pre-processing stages required to develop an effective CAD system. This work primarily aims to design an efficient CAD system for thyroid tumor characterization using ultrasound (US) images. Here, Edge Preserving Smoothing despeckling filter and encoder decoder-based ResNet50 segmentation model are used as pre-processing stages of the proposed CAD system to enhance its performance for thyroid tumor characterization. Extracting the image features using pre-trained models effectively captures the underlying textural and morphological characteristics exhibited by thyroid tumors in ultrasound images. The pre-trained- models learn by automatic feature extraction representing the underlying characteristics using multiple stages by convolution with various filters. The pre-train-based neural network classifies tumors more accurately due to learning the extract multiple sets of features. Accordingly, fifteen (15) deep learning-based pre-trained models have been utilized in the present work to extract information from the thyroid tumor US images and train the PCA-SVM classifier. These pre-train models have been taken from different categories of deep learning algorithms, including Series / DAG / Lightweight architectures, namely AlexNet, VGG16, VGG19, Darknet19, Darknet53, GoogleNet, DenseNet201, ResNet18, ResNet50, ResNet101, EfficientNetb0, NasNetMobile, MobileNet, SqueezeNet, and ShuffleNet for characterization of thyroid tissues. An exhaustive set of experiments have been conducted, and the best-performing pre-trained models have been selected as optimal feature extractors based on classification accuracy. Thus, the features extracted from the best-performing pre-trained network, i.e., ResNet50, are fed to the PCA-SVM classifier to yield an efficient CAD system for classifying TTUS images. The optimal CAD design proposed in the present work yields 99.5% classification accuracy to distinguish between benign and malignant thyroid tumors.

Ultrasound features related to thyroid lesions structure, shape, volume, and margins are considered to determine cancer risk. Automatic segmentation of the thyroid lesion would allow the sonographic features to be estimated. On the basis of clinical ultrasonography B-mode scans, a multi-output CNN-based semantic segmentation is used to separate thyroid nodules' cystic & solid components. Semantic segmentation is an automatic technique that labels the ultrasound (US) pixels with an appropriate class or pixel category, i.e., belongs to a lesion or background. In the present study, encoder-decoder-based semantic segmentation models i.e. SegNet using VGG16, UNet, and Hybrid-UNet implemented for segmentation of thyroid US images. For this work, 820 thyroid US images are collected from the DDTI and ultrasoundcases.info (USC) datasets. These segmentation models were trained using a transfer learning approach with original and despeckled thyroid US images. The performance of segmentation models is evaluated by analyzing the overlap region between the true contour lesion marked by the radiologist and the lesion retrieved by the segmentation model. The mean intersection of union (mIoU), mean dice coefficient (mDC) metrics, TPR, TNR, FPR, and FNR metrics are used to measure performance. Based on the exhaustive experiments and performance evaluation parameters it is observed that the proposed Hybrid-UNet segmentation model segments thyroid nodules and cystic components effectively.

A system to characterize normal liver, cirrhotic liver and hepatocellular carcinoma (HCC) evolved on cirrhotic liver is proposed in this paper. The study is performed with 56 real ultrasound images (15 normal, 16 cirrhotic and 25 HCC liver images) taken from 56 subjects. A total of 180 nonoverlapping regions of interest (ROIs), i.e. 60 from each image class, are extracted by an experienced participating radiologist. The multiresolution wavelet packet texture descriptors, i.e. mean, standard deviation and energy features, are computed from all 180 ROIs by using various compact support wavelet filters including Haar, Daubechies (db4 and db6), biorthogonal (bior3.1,bior3.3 and bior4.4), symlets (sym3 and sym5) and coiflets (coif1 and coif2). It is observed that a combined texture descriptor feature vector of length 48 consisting of 16 mean, 16 standard deviation and 16 energy features estimated from all 16 subband feature images (wavelet packets) obtained by second-level decomposition with two-dimensional wavelet packet transform by using Haar wavelet filter gives the best characterization performance of 86.6 %. Feature selection by genetic algorithm-support vector machine method increased the classification accuracy to 88.8 % with sensitivity of 90 % for detecting normal and cirrhotic cases and sensitivity of 86.6 % for HCC cases. Considering limited sensitivity of B-mode ultrasound for detecting HCCs evolved on cirrhotic liver, the sensitivity of 86.6 % for HCC lesions obtained by the proposed computer-aided diagnostic system is quite promising and suggests that the proposed system can be used in a clinical environment to support radiologists in lesion interpretation.

Early detection of medical renal disease is important as the same may lead to chronic kidney disease which is an irreversible stage. The present work proposes an efficient decision support system for detection of medical renal disease using small feature space consisting of only second order GLCM statistical features computed from raw renal ultrasound images. The GLCM mean feature vector and GLCM range feature vector are computed for inter-pixel distance d varying from 1 to 10. These texture feature vectors are combined in various ways yielding GLCM ratio feature vector, GLCM additive feature vector and GLCM concatenated feature vector. The present work explores the potential of five texture feature vectors computed using GLCM statistics exhaustively for differential diagnosis between normal and MRD images using SVM classifier. The result of the study indicates that GLCM range feature vector computed with d = 1 yields the highest overall classification accuracy of 85.7% with individual classification accuracy values of 93.3% and 77.9% for normal and MRD classes respectively.

Encoder-decoder-based semantic segmentation models classify image pixels into the corresponding class, such as the ROI (region of interest) or background. In the present study, simple / dilated convolution / series / directed acyclic graph (DAG)-based encoder-decoder semantic segmentation models have been implemented, i.e., SegNet (VGG16), SegNet (VGG19), U-Net, mobileNetv2, ResNet18, ResNet50, Xception and Inception networks for the segment TTUS(Thyroid Tumor Ultrasound) images. Transfer learning has been used to train these segmentation networks using original and despeckled TTUS images. The performance of the networks has been calculated using mIoU and mDC metrics. Based on the exhaustive experiments, it has been observed that ResNet50-based segmentation model obtained the best results objectively with values 0.87 for mIoU, 0.94 for mDC, and also according to radiologist opinion on shape, margin, and echogenicity characteristics of segmented lesions. It is noted that the segmentation model, namely ResNet50, provides better segmentation based on objective and subjective assessment. It may be used in the healthcare system to identify thyroid nodules accurately in real time.

Ultrasonography is widely used to screen thyroid tumors because it is safe, easy to use, and low-cost. However, it is simultaneously affected by speckle noise and other artifacts, so early detection of thyroid abnormalities becomes difficult for the radiologist. Therefore, various researchers continuously address the limitations of sonography and improve the diagnosis potential of US images for thyroid tissue from the last three decays. Accordingly, the present study extensively reviewed various CAD systems used to classify thyroid tumor US (TTUS) images related to datasets, despeckling algorithms, segmentation algorithms, feature extraction and selection, assessment parameters, and classification algorithms. After the exhaustive review, the achievements and challenges have been reported, and build a road map for the new researchers.