Modification os RBF Network Architecture

Modification os RBF Network Architectur

Pseudocolouring Enhancement Processing Of Ovarian Ultrasound Images.

Image processing may be employed on ovarian ultrasound images to assist doctors in diagnostic analysis

Multi-View Technique For 3-D Robotic Object Recognition System Using Neuro-Fuzzy Method.

The recognition of objects is one of the most challenging goals in robotic vision system. The problems increase when the process of recognition involves three dimensional (3-D) objects

Ovaidan Ultrasound Image Enhancement By Pseudocolouring.

Image processing may be employed on ovarian ultrasound images to assist doctors in diagnostic analysis. The gray levels of ovarian images are usually concentrated at the zero end of the spectrum ,making the image too low in contrast and too dark for the naked eye. This paper examines the effectiveness in displaying gray level ultrasound images as colour images and proposes a pseudocolouring approach for enhancing features in ultrasound ovarian image, which allows easy discrimination Of texture information

Classification Of Abnormal Cervical Cells Using Hierarchical Multilayered Perceptron Network.

The paper discusses the use of neural network to classify the types of cervical cells based on Bethesda system; which are normal, low-grade squamous intraepithelial lesion (LSIL) and high-grade squamous intraepithelial lesion (HSIL). The current study proposed new neural network architecture, namely hierarchical multilayered perceptron (HiMLP) network

Contrast Enhancement Image Processing Technique On Segmented Pap Smear Cytology Images.

Contrast is one of the factors that influence the accuracy of interpretation of cervical cancer cells on Pap smear images by pathologist. Blur and highly affected by unwanted noise on Pap smear images could give rise in false diagnosis rate

Classification of Acute Leukemia Based on Multilayer Perceptron

Link to publisher's homepage at https://iopscience.iop.org/In general, various artificial neural network have been applied in many areas such as modelling, pattern recognition, signal processing, diagnostic and prognostic. In this paper, artificial neural network are used to detect and classify the white blood cell (WBC) inside the acute leukemia blood samples. There are 25 features have been extracted from segmented WBC, which consist of shape, color and texture based features. Then, it have been fed up as the neural network inputs for the classification process in order to classify the segmented regions into two classes either B or T. The training algorithm for MLP network is LevenbergMarquardt (LM). The MLP network achieves the highest testing accuracy of 96.99% for 4 hidden nodes at state of 5 by using the overall 25 input features. Thus, MLP network trained by using LM algorithm is suitable for acute leukemia cells detection in blood sample

Classification of heart valve diseases using correlation analysis

Link to publisher's homepage at http://ieeexplore.ieee.orgAs a subjective and qualitative method, heart sound auscultation has it's inherit limitations. In this paper, we present an analytical perspective on heart sound auscultation and explain how to classify heart diseases using correlation analysis which is done in frequency domain. Abnormal heart sounds taken from a heart sound simulator is being cross correlated with normal heart sound to get different pattern of graph plot for each abnormality. Seven different heart valve diseases were classified with the aid of artificial neural network system. All tested data was classified correctly to their classes. It is conclude that this study is a simple and effective way to classify heart valve disorder based on heart sounds

An introduction to double stain normalization technique for brain tumour histopathological images

Stain normalization is an image pre-processing method extensively used to standardize multiple variances of staining intensity in histopathology image analysis. Staining variations may occur for several reasons, such as unstandardized protocols while preparing the specimens, using dyes from different manufacturers, and varying parameters set while capturing the digital images. In this study, a double stain normalization technique based on immunohistochemical staining is developed to improve the performance of the conventional Reinhard’s algorithm. The proposed approach began with preparing a target image by applying the contrast-limited adaptive histogram equalization (CLAHE) technique to the targeted cells. Later, the colour distribution of the input image will be matched to the colour distribution of the target image through the linear transformation process. In this study, the power-law transformation was applied to address the over-enhancement and contrast degradation issues in the conventional method. Five quality metrics comprised of entropy, tenengrad criterion (TEN), mean square error (MSE), structural similarity index (SSIM) and correlation coefficient were used to measure the performance of the proposed system. The experimental results demonstrate that the proposed method outperformed all conventional techniques. The proposed method achieved the highest average values of 5.59, 3854.11 and 94.65 for entropy, TEN, and MSE analyses

An Automated Segmentation and Counting of Ki67 Cells in Meningioma Using K-Means Clustering Technique

Link to publisher's homepage at https://iopscience.iop.org/Meningioma is a type of primary brain tumours. The meningiomas account for about one-third of all primary brain tumours. Image segmentation plays an important role in image analysis, especially detecting the tumours or cancerous areas in medical images. The output images from the segmentation prominently affect the system in detecting the tumour cells. Currently, the pathologists use the ‘eye-balling’ estimation technique to count the Ki67 cells. This technique was known as a time-saving measure. However, it has poor reliability and accuracy in counting the Ki67 cells. This paper proposed an automatic Ki67 cell counting in meningioma by using k-means clustering approach. The k-means clustering was used to segment the Ki67 cells and then the cells were classified into positive and negative Ki67 cells. The proposed system has been tested on 12 histopathological meningioma images. The proposed system is compared to the manually segmented images that have been validated in prior by the pathologists. The results show that the proposed system was able to segment the Ki67 cells with an average accuracy of 95.29%. The sensitivity and specificity of the proposed system were also high with an average of 93.56% and 97.39%, respectivel