Search

Assessing the age of an individual via bones serves as a fool proof method in true determination of individual skills. Several attempts are reported in the past for assessment of chronological age of an individual based on variety of discriminative features found in wrist radiograph images. The permutation and combination of these features realized satisfactory accuracies for a set of limited groups. In this paper, assessment of gender for individuals of chronological age between 1-17 years is performed using left hand wrist radiograph images. A fully automated approach is proposed for removal of noise persisted due to non-uniform illumination during the process of radiograph acquisition process. Subsequent to this a computational technique for extraction of wrist region is proposed using operations on specific bit planes of image. A framework called GeNet of deep convolutional neural network is applied for classification of extracted wrist regions into male and female. The experimentations are conducted on the datasets of Radiological Society of North America (RSNA) of about 12442 images. Efficiency of preprocessing and segmentation techniques resulted into a correlation of about 99.09%. Performance of GeNet is evaluated on the extracted wrist regions resulting into an accuracy of 82.18%. [ABSTRACT FROM AUTHOR]

Now a days endometrial cancer is most frequently occurring in women,endometrial is the unusual growth in uterine wall which h lead to the cancer because of the primary mutation of the most frequently mutated gene PTEN.in our proposed work we have did the gene analysis of endometrial gene for expression analysis. Endometrial cancer is the most prevalent gynecological disease, but it is still rare enough for some genetic disease syndromes to have significance as a signature cancer. Commercial multigene research panels contain up to 10 specific genes annotated in women of endometrial cancer with the germline DNA study. Many other genes from guided genome-wide sequencing trials, or multigene panel study, or work have been identified as related to family endometrial cancer. This study assesses data to support relationship with endometrial cancer risk for 32 genes involved in inherited endometrial etrial people with cancer with projeccancer The Cancer Genome Atlas and provides a list of unusual germline features in these 32 genes found by the application of integrative proteiomic profiling of endometrial-based ovarian cancer cell lines25. [ABSTRACT FROM AUTHOR]

This paper's aim is to recognize offline Hand written Malayalam character in different images using optical character recognition, OCR is achallenging technology and popular of pattern recognition.Using OCR in Malayalam language is more complex because the language contains with largest number of characters among all other Indian languages. In this paper we propose an enhanced approach of offline handwritten characters. The steps in the proposed works are Pre-processing, Segmentation. In pre-processing the method used Otsu, Sauvola and Hybrid method, which is applying dilation on sauvola method. In segmentation we use word level segmentation and character level segmentation. [ABSTRACT FROM AUTHOR]

Allocating the right job to the right person is very essential to run a huge system. In the Indian Railways there are several drivers assigned to drive a train route each day for a fixed number of hours. This task can be allocated using the Hungarian method, but the proposed method can handle the load of an additional train route being added or even the shortage of a driver and assign the duty for the same number of drivers. The workload is distributed equally among all the drivers on different days. This new method can also be used to solve any assignment problems. [ABSTRACT FROM AUTHOR]

In this study we propose a system that uses image processing techniques to predict the growth rate, fetal head recognition and head length from fetal image. In the processing of images, we use various methods to improve the image by pre-processing and to try to extract other features from the image. Pre-processing, segmentation, and extraction of features are the three general steps that all types of data will undergo while using the technique. This work focuses primarily on developing a system for predicting whether fetal growth is normal or abnormal and extracting features such as fetal duration and BPD (Biparieatal Diameter). We are doing the research trimester-wise processing of ultrasound images from various pregnant ladies in our proposed job. Using wiener filter, the ultrasound images are smoothed and the image is segmented by border cleaning thresholding, and morphological operations. [ABSTRACT FROM AUTHOR]

The initial stage of the document image is pre-processing. When we see historical document it will be degraded and it need to be preserved. Here in this paper we are considering the aged historical document of agreement copy and we are performing binarization to the document. Many methods has been proposed for binarization but there no perfect method for each every type of the document. So we propose a novel method by using both local thresholding and global thresholding and check their accuracy of the each method. We use Otsu, Niblack, sauvola, wolf methods and added morphological operation like dilation and erosion which gives extra clarity to the document. The dataset we have collect is from both Kannada and Malayalam. Promising result was seen with the accuracy of 87.03% which is better than other proposed methods for the agreement copy. [ABSTRACT FROM AUTHOR]

Assessing the age of an individual via bones serves as a fool proof method in true determination of individual skills. Several attempts are reported in the past for assessment of chronological age of an individual based on variety of discriminative features found in wrist radiograph images. The permutation and combination of these features realized satisfactory accuracies for a set of limited groups. In this paper, assessment of gender for individuals of chronological age between 1-17 years is performed using left hand wrist radiograph images. A fully automated approach is proposed for removal of noise persisted due to non-uniform illumination during the process of radiograph acquisition process. Subsequent to this a computational technique for extraction of wrist region is proposed using operations on specific bit planes of image. A framework called GeNet of deep convolutional neural network is applied for classification of extracted wrist regions into male and female. The experimentations are conducted on the datasets of Radiological Society of North America (RSNA) of about 12442 images. Efficiency of preprocessing and segmentation techniques resulted into a correlation of about 99.09%. Performance of GeNet is evaluated on the extracted wrist regions resulting into an accuracy of 82.18%. [ABSTRACT FROM AUTHOR]