CA : A Cancer Journal for Clinicians

The increasing use of biomarkers in cancer have led to the concept of personalized medicine for patients. Personalized medicine provides better diagnosis and treatment options available to clinicians. Radiological imaging techniques provide an opportunity to deliver unique data on different types of tissue. However, obtaining useful information from all radiological data is challenging in the era of ‘big data’. Recent advances in computational power and the use of genomics have generated a new area of research termed Radiomics. Radiomics is defined as the high throughput extraction of quantitative imaging features or texture (radiomics) from imaging to decode tissue pathology and creating a high dimensional data set for feature extraction. Radiomic features provide information about the gray-scale patterns, inter-pixel relationships. In addition, shape and spectral properties can be extracted within the same regions of interest on radiological images. Moreover, these features can be further used t...

Radiomics: a new application from established techniques

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.

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SVM-Based Characterization of Liver Ultrasound Images Using Wavelet Packet Texture Descriptors

Lesion segmentation is a challenging task for computer aided diagnosis systems. In this article, we propose a novel and fully automated segmentation approach for breast ultrasound (BUS) images. The major contributions of this work are: an efficient region-of-interest (ROI) generation method is developed and new features to characterize lesion boundaries are proposed. After a ROI is located automatically, two newly proposed lesion features (phase in max-energy orientation and radial distance), combined with a traditional intensity-and-texture feature, are utilized to detect the lesion by a trained artificial neural network. The proposed features are tested on a database of 120 images and the experimental results prove their strong distinguishing ability. Compared with other breast ultrasound segmentation methods, the proposed method improves the TP rate from 84.9% to 92.8%, similarity rate from 79.0% to 83.1% and reduces the FP rate from 14.1% to 12.0%, using the same database. In addition, sensitivity analysis demonstrates the robustness of the proposed method.

Completely Automated Segmentation Approach for Breast Ultrasound Images Using Multiple-Domain Features

Loss of the ability to speak or hear exerts psychological and social impacts on the affected persons due to the lack of proper communication. Multiple and systematic scholarly interventions that vary according to context have been implemented to overcome disability-related difficulties. Sign language recognition (SLR) systems based on sensory gloves are significant innovations that aim to procure data on the shape or movement of the human hand. Innovative technology for this matter is mainly restricted and dispersed. The available trends and gaps should be explored in this research approach to provide valuable insights into technological environments. Thus, a review is conducted to create a coherent taxonomy to describe the latest research divided into four main categories: development, framework, other hand gesture recognition, and reviews and surveys. Then, we conduct analyses of the glove systems for SLR device characteristics, develop a roadmap for technology evolution, discuss its limitations, and provide valuable insights into technological environments. This will help researchers to understand the current options and gaps in this area, thus contributing to this line of research.

A Review on Systems-Based Sensory Gloves for Sign Language Recognition State of the Art between 2007 and 2017.

Ultrasonography is a technique which helps the physician to visualize the structures of internal organs of human body. In some cases it is difficult to identify the boundaries of abnormal regions in the image. Hence segmentation is necessary. The manual methods of segmentation needs high attention of sonographer and also suffer from poor accuracy and time consuming. The automatic segmentation of Ultrasound images can help the physician by providing accurate location of abnormal regions. This paper describes a complete automatic technique for segmentation of masses on ultrasound images by using region growing technique. This is a best method for segmentation of ultrasound images because it is not affected by speckle noise and also preserves spatial information. In this paper the seed point has been automatically selected from the abnormal region based on textural features such as co-occurrence features and run length features. Then the threshold to control the region growing process is automatically selected and the Gradient magnitude based region growing algorithm is adopted. This method has been tested on different abdominal masses such as cyst and liver tumors. The result shows that the proposed region growing algorithm is efficient in selecting seed point as well as segmenting the region of interest without manual intervention.

http://ieeexplore.ieee.org/iel5/4155848/4155849/04155869.pdf

A complete automatic region growing method for segmentation of masses on ultrasound images

The wide spread prevalence of lost limbs and sensing system is of major concern in present day due to wars, accident, age and health problems. An intelligent wheelchair is developed to help these type of patients by using speech recognition system to control the movement of wheelchair in different directions by using voice commands and also the simple movement of the patient's fingers with keypad control. Automatic obstacle detection is done using an ultrasound system which helps the patient to apply a temporary brake in case any obstacle suddenly comes in the way of the wheelchair. The intelligent wheelchair is designed in such a way that it can be controlled easily with minimum effort from the patient and also provides protection from obstacle collision if any voice mistake happens. The main advantage is the low cost design which allows more number of patients to use this type of wheelchair.

Design and implementation of low cost intelligent wheelchair

Automatic Classification of Focal Lesions in Ultrasound Liver Images Using Combined Texture Features

Ultrasound imaging has found its own place in medical applications as an effective diagnostic tool. Ultrasonic diagnostics has made possible the detection of cysts, tumors or cancers in abdominal organs. In this paper, the possibilities of an automatic classification of ultrasonic liver images by optimal selection of texture features are explored. These features are used to classify these images into four classes-normal, cyst, benign and malignant masses. The texture features are extracted using the various statistical and signal processing methods. The automatic optimal feature selection process is based on the principal component analysis. This method extracts the principal features, or directions of maximum information from the data set. Using this new reduced feature set, the abnormalities are classified using the K-means clustering method. Based on the correct classification rate, a new optimal reduced feature set is created by combining the principal features extracted from the different texture features, to get a higher classification rate

Optimal Feature Selection and Automatic Classification of Abnormal Masses in Ultrasound Liver Images

All over world, deaf and dumb people face struggle in expressing their feelings to other people. There are various challenges experienced by speech and hearing impaired people at public places in expressing themselves to normal people. The solution to this problem is determined in this paper, by the usage of the Indian sign language symbols which are generic to all deaf and dumb people in India. The gestures illustrated by the Indian sign language symbols will be conquered with the support of the flex sensors and accelerometer. The movements included during gesture representation are rotation, angle tilt, and direction changes. The flex sensor and the accelerometer are incorporated over fingers and wrist respectively to acquire their dynamics, a these sensors are fitted over the data glove. These voltage signals will then be processed by microcontroller and sent to voice module, where the words voice outputs are stored and play backed equivalent to each word values to produce the appropriate voice words with the help of the speaker.

S. Poonguzhali

Papers

A complete automatic region growing method for segmentation of masses on ultrasound images

Design and implementation of low cost intelligent wheelchair

Automatic Classification of Focal Lesions in Ultrasound Liver Images Using Combined Texture Features

Optimal Feature Selection and Automatic Classification of Abnormal Masses in Ultrasound Liver Images

Design and development of hand gesture recognition system for speech impaired people