KLASIFIKASI CITRA MAMOGRAFI MENGGUNAKAN JARINGAN SYARAF TIRUAN

There are abnormalities in breast tissue which can be detected by mammogram images analysis. One of those abnormalities is microcalcification. Microcalcifications are small calcium deposits in the breast tissue that can be seen only on a mammogram and can be an indicator of breast cancer. The main objective of this research is to automatically recognize the pattern of two types of breast tissues, i.e. normal tissue and breast tissue which contain microcalcifications in digital mammograms using Matlab software tools.  In this research, pattern recognition is carried out using Artificial Neural Network (ANN), i.e. LVQ (Learning Vector Quantization). The pattern recognition is formulated as a supervised-learning problem and classification was based on six-feature input given to the ANN. The system recognizes the pattern in three steps. Firstly, a tophat transformation is applied on the images, and then features of the images are extracted based on images pixel values. Finally, image classification is carried out in recognizing the pattern. The research uses 26 digital mammograms, consist of 16 normal mammograms and 10 mammograms which contain microcalcifications. The results show that the LVQ best performance in recognizing the pattern is 97%, using learning rate and decrement of learning rate equal to 0.1

A cell level automated approach for quantifying antibody staining in immunohistochemistry images. A structural approach for quantifying antibody staining in colonic cancer spheroid images by integrating image processing and machine learning towards the implementation of computer aided scoring of cancer markers.

Immunohistological (IHC) stained images occupy a fundamental role in the pathologist¿s diagnosis and monitoring of cancer development. The manual process of monitoring such images is a subjective, time consuming process that typically relies on the visual ability and experience level of the pathologist. A novel and comprehensive system for the automated quantification of antibody inside stained cell nuclei in immunohistochemistry images is proposed and demonstrated in this research. The system is based on a cellular level approach, where each nucleus is individually analyzed to observe the effects of protein antibodies inside the nuclei. The system provides three main quantitative descriptions of stained nuclei. The first quantitative measurement automatically generates the total number of cell nuclei in an image. The second measure classifies the positive and negative stained nuclei based on the nuclei colour, morphological and textural features. Such features are extracted directly from each nucleus to provide discriminative characteristics of different stained nuclei. The output generated from the first and second quantitative measures are used collectively to calculate the percentage of positive nuclei (PS). The third measure proposes a novel automated method for determining the staining intensity level of positive nuclei or what is known as the intensity score (IS). The minor intensity features are observed and used to classify low, intermediate and high stained positive nuclei. Statistical methods were applied throughout the research to validate the system results against the ground truth pathology data. Experimental results demonstrate the effectiveness of the proposed approach and provide high accuracy when compared to the ground truth pathology data

N.: Support vector machine learning for detection of microcalcifications in mammograms

Microcalcification (MC) clusters in mammograms can be an indicator of breast cancer. In this work we propose for the first time the use of support vector machine (SVM) learning for automated detection of MCs in digitized mammograms. In the proposed framework, MC detection is formulated as a supervised-learning problem and the method of SVM is employed to develop the detection algorithm. The proposed method is developed and evaluated using a database of 76 mammograms containing 1120 MCs. To evaluate detection performance, free-response receiver operating characteristic (FROC) curves are used. Experimental results demonstrate that, when compared to several other existing methods, the proposed SVM framework offers the best performance. 1