Breast Cancer Essay

Secondly, 187 descriptor features of lesions from ABCD criteria and texture feature are used, and the value of extracted descriptors are

Segmentation evaluation is the task of comparing two segmentations by measuring the distance or similarity between them where one is the segmentation to be evaluated and the other is the corresponding groundtruth segmentation. The performance of the proposed SM is validated with breast overlapping metrics such as Dice Index (DI), the Jaccard index (JI), false positive (FP) and false negative (FN) errors. The DI [43] [44], also called the overlap index, is the most used metric in validating medical segmentation results. It provides a direct comparison between automatic and ground truth segmentations. It can be calculated by using the following equation:

The U.S. National Institutes of Health (NIH) are funding new research that aims to develop novel imaging techniques to detect both breast and ovarian cancers early and more accurately. Cancer is typically difficult or even impossible to see, which is why cancer detection as well as diagnosis (let alone treatment) has historically proven to be so challenging. A separate study has already been published in Radiology covering the amalgamation of synthetic 2-D imaging and digital breast tomosynthesis (DBT), and it observes measurably improved rates of cancer detection in comparison to the usual, full-field digital mammography (FFDM).

When segmentation is performed on an image, the goal is to divide the image into smaller non-overlapping sections that encapsulate a type of component. This can be accomplished in many ways. Thresholding, for example, simply separates objects based on their color value. Edge-based segmentation classify objects by first using an edge filter. This approach “is usually less than perfect. Often, a scientist will have to make changes to the results of automatic segmentation” (Glasbey & Horgan, p. 21, 1995). Region-based segmentation group items based on similar properties and proximity. Sometimes textures can disrupt these regions, which is problematic because “for most natural textures, simple statistical measures are of little use” (Delmas). It might be possible to improve results by combining all of these methods, but none of them consider the larger meaning of the images they process.

In recent years, for these reasons and more there has been increasing interest in employing various image analysis techniques for classification of HEP-2 cells images. Many CAD systems which have been proposed in this field are concerned with shape and morphological features [10-23].

Abstract-This paper presents the approach to analyze the ultrasound images of the patients suffering from Cholelithiasis. The occurrence of Cholelithiasis is the commonest biliary disease to be reported in India. Our research is aimed to apply the potential of image processing in diagnosing the presence of gall bladder stones. In this paper we propose a technique, a combination of preprocessing morphological techniques and Entropy calculation of the pixels representing gallstones in the gall bladder.

Breast cancer, behind skin cancer is the leading cause of death among women and is responsible for more than 40,000 deaths every year. Many of these death may result from inaccurate or late diagnosis. It is a well-known fact that the technology of today is not capable of curing cancer. But, it is known that when cancer is detected at an early stage it can be treated much quicker, leading up to a higher chance of a prolonged life. For this reason, radiologists want to detect breast cancer at an early stage (Ganesan 2014).A successful breast cancer diagnosis requires systematic image analysis, characterization, and integration of many clinical and mammographic variables. "An ideal diagnostic system would discriminate

Mei wang, Xiao-Wei Wu, Hsiung-Cheng Lin, Jian-ping wang(2012) discussed the idea of following. Today medical images are widely used. To achieve a clear and noise free images Image-segmentation is essential point. In this paper a new image segmentation method that is proportion of foreground to background is used also called PFB. Using this method on human brain image we experiment the result that the number of iteration steps for the threshold T is reduced. Using this method over segmentation and discontinuities also can be avoided. Finally proposed a new method using both Thresholding image segmentation and edge detection segmentation algorithm based on the Proportion of Foreground to Background. After this the basic principal is define in this paper. Proportion of Foreground to Background Algorithm combine the traditional iterative algorithm.

Features are essential for any classification or analysis in image processing. There are many types of features which can be extracted from the images each gives its identical informations about the image. Here MA region supposed with properties such as shape, color and size which appears as a dark red colored circle shape. To identify MA and non- MA region feature vectors are formed for each candidate regions.

Abstract— Photographing the changes in internal breast structure due to formation of masses and microcalcification for detection of Breast Cancer is known as Mammogram, which are low dose x-ray images. These images play a very significant role in early detection of breast cancer. Usually in pattern recognition texture analysis is used for classification based on content of image or in image segmentation based on variation of intensities of gray scale levels or colours. Similarly texture analysis can also be used to identify masses and microcalcification in mammograms. However Grey Level Co-occurrence Matrices (GLCM) technique introduced by Haralick was initially used in study of remote sensing images. Up till now in breast cancer detection only first and second order GLCM features were mostly used, to the best of our knowledge there is no evidence of use of higher order GLCM features for detection of malignant masses in breast tissue images. In this paper we attempted up to 7th order and observed the results by analyzing the effects of higher order features in recognition of malignancy in

The challenge faced by any existing imaging is to distinguish the malignant breast tissues from healthy fibro glandular tissue regions. Another compelling potential application is the characterization of the relative density of tissues in a healthy breast using microwave imaging as the high breast density is strongly correlated with cancer risk. The breast density estimation via microwave imaging can help to identify high-risk subjects before cancer has manifested. In addition, microwave imaging can also be used in monitoring the response of a tumour to other treatment protocols. These require dielectric mapping of the breast in addition to localization of malignant tissue.

This project presents an automated system of classification of tumor from brain MRI. The algorithm uses T2-weighted MRI images. The useful and important features of image are extracted from medical image for classification purpose. Here texture features are extracted using Gray Level Co-occurrence Matrix (GLCM) method. The classification of MR images is done using Adaboost classifier. Then finally the performance of classifier is evaluated by sensitivity, specificity, error rate and accuracy.

The history of development of automated image analysis technology: scientists has done the study since 1920, start for application on 1960, the range of application expanded rapidly after 1970, and nowadays: the application of image analysis technology in almost every fields of nature science. Of course, Image analysis also can be used in medical science for histology tissue study().

Feature extraction is the third stage in medical image processing application, after image pre-processing. In feature extraction, the features like the shape, colour, texture are used to describe an image content[bio2].features can be short relevance or strong relevant ones. Short relevant features give only little information about the image, while strong relevant features provide significant information about the image. Finding these strong relevant features are time consuming and hence good techniques has to be developed.

Feature plays a very important role in the area of image processing. Different feature extraction