Osman N. Ucan

Mammographical mass detection and classification using Local Seed Region Growing-Spherical Wavelet Transform (LSRG-SWT) hybrid scheme

The purpose of this study is to implement accurate methods of detection and classification of benign and malignant breast masses in mammograms. Our new proposed method, which can be used as a diagnostic tool, is denoted Local Seed Region Growing-Spherical Wavelet Transform (LSRG-SWT), and consists of four steps. The first step is homomorphic filtering for enhancement, and the second is detection of the region of interests (ROIs) using a Local Seed Region Growing (LSRG) algorithm, which we developed. The third step incoporates Spherical Wavelet Transform (SWT) and feature extraction. Finally the fourth step is classification, which consists of two sequential components: the 1st classification distinguishes the ROIs as either mass or non-mass and the 2nd classification distinguishes the masses as either benign or malignant using a Support Vector Machine (SVM). The mammograms used in this study were acquired from the hospital of Istanbul University (I.U.) in Turkey and the Mammographic Image Analysis Society (MIAS). The results demonstrate that the proposed scheme LSRG-SWT achieves 96% and 93.59% accuracy in mass/non-mass classification (1st component) and benign/malignant classification (2nd component) respectively when using the I.U. database with k-fold cross validation. The system achieves 94% and 91.67% accuracy in mass/non-mass classification and benign/malignant classification respectively when using the I.U. database as a training set and the MIAS database as a test set with external validation.

Computer-aided classification of breast masses in mammogram images based on spherical wavelet transform and support vector machines

Breast cancer can be effectively detected and diagnosed using the technology of digital mammography. However, although this technology has been rapidly developing recently, suspicious regions cannot be detected in some cases by radiologists, because of the noise or inappropriate mammogram contrast. This study presents a classification of segmented region of interests ROIs as either benign or malignant to serve as a second eye of the radiologists. Our study consists of three steps. In the first step, spherical wavelet transform SWT is applied to the original ROIs. In the second step, shape, boundary and grey level based features of wavelet detail and scaling approximation coefficients are extracted. Finally, in the third step, malignant/benign classification of the masses is implemented by giving the feature matrices to a support vector machine system. The proposed system achieves 91.4% and 90.1% classification accuracy using the dataset acquired from the hospital of Istanbul University in Turkey and the free Mammographic Image Analysis Society, respectively. Furthermore, discrete wavelet transform, which produces 83.3% classification accuracy, is applied to the coefficients to make a comparison with the SWT method.

Cooperative communications with multilevel/AES-SD4-CPFSK in wireless sensor networks

In this paper, a new joint multilevel data encryption and channel coding mechanism is proposed, which is called “multilevel/advanced encryption standard–systematic distance 4–continuous phase frequency shift keying” (ML/AES-SD4-CPFSK). In the proposed scheme, we have not only taken advantage of spatial diversity gains but also optimally allocated energy and bandwidth resources among sensor nodes as well as providing high level of security and error protection for cooperative communications in wireless sensor networks. Relay protocols of cooperative communications, such as amplify-and-forward and decode-and-forward with/without adversary nodes, have been studied for 4CPFSK, 8CPFSK, and 16CPFSK of ML/AES-SD4-CPFSK. We have evaluated the error performances of multilevel AES for data encryption, multilevel SD-4 for channel coding, and various CPFSK types for modulation utilizing cooperative communications in wireless sensor networks. According to computer simulation results, significant diversity gain and coding gain have been achieved. As an example, bit error rate (BER) performance of 10−5 value has been obtained at a signal-to-noise ratio (SNR) of −6xa0dB for SD-4-CPFSK scheme in a compared related journal paper, whereas in our proposed system, we have reached the same BER value at a SNR of −23xa0dB with amplify-and-forward with direct path signal protocol in 16-level AES, two-level SD-4 coded 16CPFSK, and at the same time, we have reached the same BER value at a SNR of −22xa0dB with amplify-and-forward without direct path signal protocol in 16-level AES, two-level SD-4 coded 16CPFSK.