Digital Image Processing

Pradeep Kumar , Sudhir Kumar Sharma , Sidheshwar Prasad

Abstract

The fetal electrocardiogram (FECG) signal is outcome of the electrical activity of the fetal heart after 21 days of the pregnancy. It contains information about the health status of the fetus and so, an early diagnosis of any cardiac defects before delivery (Specially in case of labour pain) increases the chance of the appropriate treatment. In this paper we consider one signal from the thoracic and another from abdomen of the mother. The artificial neural network fuzzy inference system (ANFIS) is used to obtain the FECG component from abdominal ECG recording and reference thoracic maternal electrocardiogram (MECG) signal. The obtained FECG is being enhanced by using wavelet transform.

Keywords

ECG, MECG, FECG, Neural Network, Fuzzy Logic, Membership Function and Wavelet Transform.

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Pradeep Kumar ¹, R. Sureshbabu ¹

Affiliations
1 Jaya Engineering College, Chennai-602024, IN

Abstract

Mammography is the most effective method for the early detection of breast diseases. However, high accurate detection of masses and micro-classification in mammogram is critical for improving the performance and efficiency of computer-aided diagnosis (CAD) system due low-contrast and noisy images. In this paper, we propose a novel approach to enhance the detection performance of mass in mammograms using Wavelet Transform Modulus Maximum (WTMM). First, hunt the region of interest (ROI) through the whole image and the ROI was approximately located by multi-threshold method. Then the contour of the ROI was extracted from the modulus image acquired by WTMM method. The region of interest was finally refined by the contour extracted. The proposed method has been tested on a large number of clinical images, comparing the results with those obtained by several other algorithms proposed in the literature through both analytical indexes and the opinions of radiologists. Experimental results indicate that the proposed method is able to detect not only isolate masses, but also the masses connected with the glandular tissues successfully. This technique could potentially improve the performance of CAD system and diagnosis accuracy in mammograms.

Keywords

Denoising, Image Enhancement, Mass and Micro-Classification Detection, Region of Interest, Segmentation of ROI, WTMM.

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