Gray Image extraction using Fuzzy Logic

Fuzzy systems concern fundamental methodology to represent and process uncertainty and imprecision in the linguistic information. The fuzzy systems that use fuzzy rules to represent the domain knowledge of the problem are known as Fuzzy Rule Base Systems (FRBS). On the other hand image segmentation and subsequent extraction from a noise-affected background, with the help of various soft computing methods, are relatively new and quite popular due to various reasons. These methods include various Artificial Neural Network (ANN) models (primarily supervised in nature), Genetic Algorithm (GA) based techniques, intensity histogram based methods etc. providing an extraction solution working in unsupervised mode happens to be even more interesting problem. Literature suggests that effort in this respect appears to be quite rudimentary. In the present article, we propose a fuzzy rule guided novel technique that is functional devoid of any external intervention during execution. Experimental results suggest that this approach is an efficient one in comparison to different other techniques extensively addressed in literature. In order to justify the supremacy of performance of our proposed technique in respect of its competitors, we take recourse to effective metrics like Mean Squared Error (MSE), Mean Absolute Error (MAE), Peak Signal to Noise Ratio (PSNR).Comment: 8 pages, 5 figures, Fuzzy Rule Base, Image Extraction, Fuzzy Inference System (FIS), Membership Functions, Membership values,Image coding and Processing, Soft Computing, Computer Vision Accepted and published in IEEE. arXiv admin note: text overlap with arXiv:1206.363

Fair comparison of skin detection approaches on publicly available datasets

Skin detection is the process of discriminating skin and non-skin regions in a digital image and it is widely used in several applications ranging from hand gesture analysis to track body parts and face detection. Skin detection is a challenging problem which has drawn extensive attention from the research community, nevertheless a fair comparison among approaches is very difficult due to the lack of a common benchmark and a unified testing protocol. In this work, we investigate the most recent researches in this field and we propose a fair comparison among approaches using several different datasets. The major contributions of this work are an exhaustive literature review of skin color detection approaches, a framework to evaluate and combine different skin detector approaches, whose source code is made freely available for future research, and an extensive experimental comparison among several recent methods which have also been used to define an ensemble that works well in many different problems. Experiments are carried out in 10 different datasets including more than 10000 labelled images: experimental results confirm that the best method here proposed obtains a very good performance with respect to other stand-alone approaches, without requiring ad hoc parameter tuning. A MATLAB version of the framework for testing and of the methods proposed in this paper will be freely available from https://github.com/LorisNann

Phenotypic and molecular characterization root system architecture in diverse soybean (Glycine max L. Merr.) accessions

Root system architecture (RSA), or the spatial arrangement of the root and its morphology, functions to anchor the plant, provide water and nutrient acquisition, nutrient storage and to facilitate plant-microbe interactions such as nodulation in legumes such as soybean [Glycine max L. Merr.)]. Root structure also correlates to environmental advantages, such as nutrient acquisition, drought, flood tolerance, and lodging resistance. After centuries of indirect selection for RSA, there is a focus to harness soybean RSA diversity for exploitation and implementation into cultivar development programs. Researchers have generally taken one of three strategies to approach root phenotyping including controlled laboratory, moderately controlled greenhouse and minimally controlled field methods. In this study we developed a mobile, low-cost, and high-resolution root phenotyping system composed of an imaging platform with computer vision and ML based approaches to establish a seamless end-to-end pipeline. This system provides a high-throughput, cost effective, non-destructive methodology that delivers biologically relevant time-series data on root growth and development for phenomics, genomics, and plant breeding applications. We customized a previous version of the Automated Root Imaging Analysis root phenotyping software. New modifications to the workflow allow integrates time series image capture coupled with automated image processing that uses optical character recognition to identify barcodes, followed by segmentation using a convolutional neural network. The goal of this research was to study the root trait genetic diversity in soybean using 292 soybean accessions from the USDA core collection primarily in maturity group II and III and a subset of the soybean nested association mapping (NAM) parents. Combining 35,448 SNPs with a semi-automated phenotyping platform, these 292 accessions were studied for RSA traits to decipher the genetic diversity and explore informative root (iRoot) categories based on current literature for root shape categories. Genotype- and phenotype-based hierarchical clusters were found from the diverse set with significant correlations. Genotype based clusters correlated with geographical origins, and genetic differentiation indicated that much of US origin genotypes do not possess genetic diversity for RSA traits. Results show that superior root performance and root shape also correlate to specific genomic clusters. This combination of genetic and phenotypic analyses results provides opportunities for targeted breeding efforts to maximize the beneficial genetic diversity for future genetic gains. Further objectives of this study was to identify genetic control of RSA within the diverse soybean landscape as well as determine whether a genomic prediction could be a viable strategy for breeding for root architecture traits. The GWAS detected 30 SNPs which co-located within previously identified QTL for root traits and identified a number of root development gene candidates. The GP model is capable of predicting phenotypes based on genomic data allowing selection of individuals with root traits of interest within the core collection without utilizing phenotypic data. Plant phenomics coupled with molecular technologies and statistical approaches identify genotypes with favorable or unfavorable traits, allowing for inexpensive selections prior to field trial phenotyping. Employment of these genomic and phenomic technologies will allow soybean breeders to vastly expand the scope of a breeding program

A multi-view approach to cDNA micro-array analysis

The official published version can be obtained from the link below.Microarray has emerged as a powerful technology that enables biologists to study thousands of genes simultaneously, therefore, to obtain a better understanding of the gene interaction and regulation mechanisms. This paper is concerned with improving the processes involved in the analysis of microarray image data. The main focus is to clarify an image's feature space in an unsupervised manner. In this paper, the Image Transformation Engine (ITE), combined with different filters, is investigated. The proposed methods are applied to a set of real-world cDNA images. The MatCNN toolbox is used during the segmentation process. Quantitative comparisons between different filters are carried out. It is shown that the CLD filter is the best one to be applied with the ITE.This work was supported in part by the Engineering and Physical Sciences Research Council (EPSRC) of the UK under Grant GR/S27658/01, the National Science Foundation of China under Innovative Grant 70621001, Chinese Academy of Sciences under Innovative Group Overseas Partnership Grant, the BHP Billiton Cooperation of Australia Grant, the International Science and Technology Cooperation Project of China under Grant 2009DFA32050 and the Alexander von Humboldt Foundation of Germany

Deep Functional Mapping For Predicting Cancer Outcome

The effective understanding of the biological behavior and prognosis of cancer subtypes is becoming very important in-patient administration. Cancer is a diverse disorder in which a significant medical progression and diagnosis for each subtype can be observed and characterized. Computer-aided diagnosis for early detection and diagnosis of many kinds of diseases has evolved in the last decade. In this research, we address challenges associated with multi-organ disease diagnosis and recommend numerous models for enhanced analysis. We concentrate on evaluating the Magnetic Resonance Imaging (MRI), Computed Tomography (CT), and Positron Emission Tomography (PET) for brain, lung, and breast scans to detect, segment, and classify types of cancer from biomedical images. Moreover, histopathological, and genomic classification of cancer prognosis has been considered for multi-organ disease diagnosis and biomarker recommendation. We considered multi-modal, multi-class classification during this study. We are proposing implementing deep learning techniques based on Convolutional Neural Network and Generative Adversarial Network. In our proposed research we plan to demonstrate ways to increase the performance of the disease diagnosis by focusing on a combined diagnosis of histology, image processing, and genomics. It has been observed that the combination of medical imaging and gene expression can effectively handle the cancer detection situation with a higher diagnostic rate rather than considering the individual disease diagnosis. This research puts forward a blockchain-based system that facilitates interpretations and enhancements pertaining to automated biomedical systems. In this scheme, a secured sharing of the biomedical images and gene expression has been established. To maintain the secured sharing of the biomedical contents in a distributed system or among the hospitals, a blockchain-based algorithm is considered that generates a secure sequence to identity a hash key. This adaptive feature enables the algorithm to use multiple data types and combines various biomedical images and text records. All data related to patients, including identity, pathological records are encrypted using private key cryptography based on blockchain architecture to maintain data privacy and secure sharing of the biomedical contents

Computer vision and machine learning enabled soybean root phenotyping pipeline

Background Root system architecture (RSA) traits are of interest for breeding selection; however, measurement of these traits is difficult, resource intensive, and results in large variability. The advent of computer vision and machine learning (ML) enabled trait extraction and measurement has renewed interest in utilizing RSA traits for genetic enhancement to develop more robust and resilient crop cultivars. We developed a mobile, low-cost, and high-resolution root phenotyping system composed of an imaging platform with computer vision and ML based segmentation approach to establish a seamless end-to-end pipeline - from obtaining large quantities of root samples through image based trait processing and analysis. Results This high throughput phenotyping system, which has the capacity to handle hundreds to thousands of plants, integrates time series image capture coupled with automated image processing that uses optical character recognition (OCR) to identify seedlings via barcode, followed by robust segmentation integrating convolutional auto-encoder (CAE) method prior to feature extraction. The pipeline includes an updated and customized version of the Automatic Root Imaging Analysis (ARIA) root phenotyping software. Using this system, we studied diverse soybean accessions from a wide geographical distribution and report genetic variability for RSA traits, including root shape, length, number, mass, and angle. Conclusions This system provides a high-throughput, cost effective, non-destructive methodology that delivers biologically relevant time-series data on root growth and development for phenomics, genomics, and plant breeding applications. This phenotyping platform is designed to quantify root traits and rank genotypes in a common environment thereby serving as a selection tool for use in plant breeding. Root phenotyping platforms and image based phenotyping are essential to mirror the current focus on shoot phenotyping in breeding efforts