Iris-based Image Processing for Cholesterol Level Detection using Gray Level Co-Occurrence Matrix and Support Vector Machine

Serious illnesses such as strokes and heart attacks can be triggered by high levels of cholesterol in human blood that exceeds ideal conditions, where the ideal cholesterol level is below 200 mg/dL. To find out cholesterol levels need a long process because the patient must go through a blood sugar test that requires the patient to undergo fasting for 10–12 hours first before the test. Iridology is a branch of science that studies human iris and its relation to the wellness of human internal organs. The method can be used as an alternative for medical analysis. Iridology thus can be used to assess the conditions of organs, body construction, and other psychological conditions. This paper proposes a cholesterol detection system based on the iris image processing using Gray Level Co-Occurrence Matrix (GLCM) and Support Vector Machine (SVM). GLCM is used as the feature extraction method of the image, while SVM acts as the classifier of the features. In addition to GLCM and SVM, this paper also construct a preprocessing method which consist of image resizing, segmentation, and color image to gray level conversion of the iris image. These steps are necessary before the GLCM feature extraction step can be applied. In principle, the GLCM method is a construction of a matrix containing the information about the proximity position of gray level images pixels. The output of GLCM is fed to the SVM that relies on the best hyperplane. Thus, SVM performs as a separator of two data classes of the input space. From the simulation results, the system built was able to detect excess cholesterol levels through iris image and classify into three classes, namely: non–cholesterol (< 200 ), risk of cholesterol (200–239 ) and high cholesterol (> 240 ). The accuracy rate obtained was 94.67% with an average computation time of 0.0696 . It was using each of the 75 training and test data, with the second-order parameters used are contrast–correlation–energy–homogeneity, pixel distance = 1, quantization level =  8, Polynomial kernel types and One Against One Multiclass. Iris has specific advantages which can record all organ conditions, body construction and psychological conditions. Therefore, Iridology as a science based on the arrangement of iris fibers can be an alternative for medical analysis. In this paper proposes a cholesterol detection system through the iris using Gray Level Co-occurence Matrix and Support Vector Machine. Input system is an iris image that will be processed by pre-processing and then extracted features with the Gray Level Co-Occurrence Matrix method which is a matrix containing information about position the proximity of pixels that have a certain gray level. And then classified with the Support Vector Machine method that relies on the best hyper lane which functions as a separator of two data classes in the input space. From the simulation results, the system built was able to detect excess cholesterol levels through iris image and classify into three classes are: risk of cholesterol, high cholesterol and non–cholesterol with an accuracy rate of 96.47% and average computation time was 0.0696  using each of the 75 training and test data, with the second-order parameters used are contrast–correlation–energy–homogeneity, pixel distance = 1, quantization level =  8, Polynomial kernel types and One Against One Multiclas

Ensemble of convolutional neural networks for bioimage classification

This work presents a system based on an ensemble of Convolutional Neural Networks (CNNs) and descriptors for bioimage classification that has been validated on different datasets of color images. The proposed system represents a very simple yet effective way of boosting the performance of trained CNNs by composing multiple CNNs into an ensemble and combining scores by sum rule. Several types of ensembles are considered, with different CNN topologies along with different learning parameter sets. The proposed system not only exhibits strong discriminative power but also generalizes well over multiple datasets thanks to the combination of multiple descriptors based on different feature types, both learned and handcrafted. Separate classifiers are trained for each descriptor, and the entire set of classifiers is combined by sum rule. Results show that the proposed system obtains state-of-the-art performance across four different bioimage and medical datasets. The MATLAB code of the descriptors will be available at https://github.com/LorisNanni

DETECÇÃO E DIAGNÓSTICO DE MASSAS EM MAMOGRAFIA: revisão bibliográfica

Resumo: O câncer de mama tem se tornado cada dia mais freqüente entre a população feminina acima dos 40 anos. Somente para o ano de 2011 são estimados, no Brasil, 49 mil novos casos. Uma das maneiras para detectar os tumores não palpáveis que causam câncer de mama é realizar uma radiografia (mamografia) das mamas. A  mamografia é atualmente a melhor técnica de detecção precoce de lesões não apalpáveis na mama com altas chances de ser um câncer curável. Sabe-se que as chances de cura do câncer de mama são, relativamente altas, se detectado nos estágios inicias. Entretanto, a sensibilidade desse exame pode variar bastante, em decorrência de fatores como qualidade do exame ou experiência do especialista. Dessa forma, a utilização de sistemas CAD e CADx tem contribuído para aumentar as chances de uma detecção e diagnósticos corretos, ou seja, uma segunda opinião, auxiliando os especialistas na tomada de decisões em um tratamento do câncer de mama. Este artigo faz uma revisão bibliográfica de trabalhos voltados para detecção e diagnóstico de massas.Palavras-chave: Massa. Mamografia. Detecção. Diagnóstico. Câncer de mama.MAMMOGRAPHY MASS DETECTION AND DIAGNOSIS: a surveyAbstract: Breast cancer has become increasingly common among the female population over 40 years old. Only for the year 2011 are estimated, in Brazil, 49 000 new cases. One way to detect non-palpable tumors that cause breast cancer is to perform an X-ray (mammogram) of the breasts. Mammography is currently the best technique for early detection of non-palpable breast lesions with high chances of being a curable cancer. It is known that the chances of a cure for breast cancer are relatively high if detected in early stages. However, the sensitivity of this exam can vary greatly due to factors such as quality of examination or experience of the specialist. Thus, the use of CAD systems and CADX has contributed to increase the chances of detection and correct diagnosis, working as a second opinion in treatment of breast cancer. This article is a literature review of studies focused on detection and diagnosis of masses.Keywords: Mass. Mammography. Detection. Diagnosis. Breast cancer.DETECCIÓN Y DIAGNÓSTICO DE MASAS EN UNA MAMOGRAFÍA: una revisión de la literatura Resumen: El cáncer de mama se ha tornado cada vez más común entre la población femenina de más de 40 años. Sólo para el año 2011 se estima que en Brasil habrán 49 000 nuevos casos. Una forma de detectar tumores no palpables que causan el cáncer de mama es realizar una radiografía (mamografía) de los senos. La mamografía es actualmente la mejor técnica para la detección precoz de lesiones mamarias no palpables, con altas posibilidades de ser un cáncer curable. Se sabe que las posibilidades de una cura para el cáncer de mama son relativamente altas si se detecta en etapas tempranas. Sin embargo, la sensibilidad de esta prueba pueden variar considerablemente debido a factores como la calidad de los exámenes o la experiencia del especialista. Por lo tanto, el uso de sistemas CAD y CADX ha contribuido a aumentar las posibilidades de  detección y el diagnóstico correcto, o una segunda opinión, ayudando a los expertos en la tomada de decisiones en el tratamiento del cáncer de mama. Este artículo es una revisión de la literatura de trabajos sobre detección y diagnóstico de masas.Palabras clave: Masa. Mamografía. Detección. Diagnóstico de cáncer de mama

Using Synchronized Audio Mapping to Predict Velar and Pharyngeal Wall Locations during Dynamic MRI Sequences

Automatic tongue, velum (i.e., soft palate), and pharyngeal movement tracking systems provide a significant benefit for the analysis of dynamic speech movements. Studies have been conducted using ultrasound, x-ray, and Magnetic Resonance Images (MRI) to examine the dynamic nature of the articulators during speech. Simulating the movement of the tongue, velum, and pharynx is often limited by image segmentation obstacles, where, movements of the velar structures are segmented through manual tracking. These methods are extremely time-consuming, coupled with inherent noise, motion artifacts, air interfaces, and refractions often complicate the process of computer-based automatic tracking. Furthermore, image segmentation and processing techniques of velopharyngeal structures often suffer from leakage issues related to the poor image quality of the MRI and the lack of recognizable boundaries between the velum and pharynx during contact moments. Computer-based tracking algorithms are developed to overcome these disadvantages by utilizing machine learning techniques and corresponding speech signals that may be considered prior information. The purpose of this study is to illustrate a methodology to track the velum and pharynx from a MRI sequence using the Hidden Markov Model (HMM) and Mel-Frequency Cepstral Coefficients (MFCC) by analyzing the corresponding audio signals. Auditory models such as MFCC have been widely used in Automatic Speech Recognition (ASR) systems. Our method uses customized version of the traditional approach for audio feature extraction in order to extract visual feature from the outer boundaries of the velum and the pharynx marked (selected pixel) by a novel method, The reduced audio features helps to shrink the search space of HMM and improve the system performance.   Three hundred consecutive images were tagged by the researcher. Two hundred of these images and the corresponding audio features (5 seconds) were used to train the HMM and a 2.5 second long audio file was used to test the model. The error rate was measured by calculating minimum distance between predicted and actual markers. Our model was able to track and animate dynamic articulators during the speech process in real-time with an overall accuracy of 81% considering one pixel threshold. The predicted markers (pixels) indicated the segmented structures, even though the contours of contacted areas were fuzzy and unrecognizable.  M.S