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    Bölütlenen beyin bölgelerinin tıbbi görüntü steganografi için değerlendirilmesi
    (Gazi Üniversitesi, 2021) Karakış, Rukiye; Gurkahraman, Kali; Çiğdem, Burhanettin; Oztoprak, Ibrahim; Topaktas, A. Suat
    Tıbbi görüntü steganografisinde, görüntülere veri gizlemenin neden olduğu bozulmanın sonucunda bir hastalığın tanı ve tedavisi etkilenebilir. Bu sebeple, veri görüntülerde elle ya da eşikleme gibi temel tekniklerle belirlenen ilgi olmayan bölgelerde gizlenmektedir ve bu yöntemlerin hiçbiri tümör gibi dokuları bölütlemeyi içermemektedir. Bu çalışma, bir hastalığın tanı ve tedavisinde kullanılan verilerin, bölütleme tabanlı steganografi yöntemi ile görüntüleri bozmadan tek bir ortamda birleştirilerek gizlenmesini amaçlamaktadır. Ayrık dalgacık dönüşümü (ADD) ve k-ortalama kümeleme tabanlı bölütleme yöntemi ile epilepsi hastalarının Manyetik Rezonans (MR) görüntüleri, arka plan, gri madde, beyaz madde ve tümör olarak ayrıştırılmıştır. Gizli mesaj, hasta kişisel bilgilerini, doktor yorumunu, seçilen Elektroansefalogram (EEG) sinyalini ve EEG’ye ait sağlık raporunu içermektedir. Kaotik ve hash fonksiyonlarını kullanan DNA kodlama ile şifrelenen ve ardından sıkıştırılan yüksek kapasiteli mesaj, görüntülerin tümör olmayan piksellerinin en az anlamlı bitlerinde gizlenmiştir. Çalışmada, taşıyıcı ve stego görüntüler arasındaki farklılık, sinyalin gürültü tepe oranı, yapısal benzerlik ölçümü, evrensel kalite indeksi ve korelasyon katsayısı ile tespit edilmiştir. Bu değerler sırasıyla 64,0334 desibel (dB), 0,9979, 0,99701, 0,9993 olarak elde edilmiştir. Analiz sonuçları önerilen yöntemin hastaların yüksek kapasiteli verilerini tek bir dosyada birleştirdiğini ve tıbbi verilerin hem güvenliğini hem de kayıt alanını arttırdığını göstermiştir.
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    Evaluation of segmented brain regions for medical image steganography
    (Gazi Univ, Fac Engineering Architecture, 2021) Karakis, Rukiye; Gurkahraman, Kali; Cigdem, Burhanettin; Oztoprak, Ibrahim; Topaktas, A. Suat
    In medical image steganography, diagnosis and treatment of a disease can be affected as a result of the distortion caused by the embedding data in the images. For this reason, data is embedded in the region of non-interest determined by basic techniques such as manual or thresholding, and none of these methods involve the segmentation of brain tissues such as tumours. The present study aims to hide the data used in the diagnosis and treatment of a disease without affecting the medical information in the images with a segmentation-based steganography method by combining them into one file format. Magnetic Resonance (MR) images of epilepsy patients were segmented as background, gray matter, white matter, and tumour by discrete wavelet transform (DWT) and k-means clustering-based segmentation method. The hidden data includes confidential patient information, doctor's comment, selected Electroencephalogram (EEG) signals, and EEG health reports. The high-capacity message, which encoded by DNA encryption using chaotic and hash functions, and then compressed, is hidden in the least significant bits of non-tumour pixels of images. In the study, the difference between the cover and the stego images was measured by the peak signal-to-noise ratio, the structural similarity measure, the universal quality index, and the correlation coefficient. These values were obtained as 64.0334 decibels (dB), 0.9979, 0.9971, 0.9993, respectively. A comparison of the results indicates that the proposed method combines the high capacity data of the patients in a single file format and increases both the security and recording space of medical data.
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    The Effect of Sad and Cheerful Music Samples on Short Term Memory
    (Hayrullah KAHYA, 2021) Toptaş, Hazal; Erdal, Barış; Tepe, Yeliz Kındap; Çiğdem, Burhanettin; Topaktas, A. Suat
    The study investigated listening cheerful versus sad music samples on Heart Rate Variability (HRV) and short-term memory performance. For this purpose, a random sample comprised of 65 university students (33 males (50.8%) and 32 females (49.2%)) enrolled in music education programs was gathered. The average age of the participants is 21.68 (S = 2.48, range = 18-30). To assess memory performance, the study utilized Number Sequence Learning Test with reverse and straight coding, coupled with Heart Rate Variability (HRV) analysis to assess the autonomic nervous system activation. The analyses led to the observation that the heart rate and LF/HF levels when listening to cheerful music were higher than the heart rate and LF/HF levels when listening sad music. Furthermore, HF when listening to sad music was found to be higher than HF when listening to cheerful music. HRV figures prior to listening to music, on the other hand, did not vary. The increase in high frequency (HF) figures when listening to sad music is deemed to be an indicator of parasympathetic activation. The HRV values after listening to cheerful music did not exhibit variation based on gender. However, the HRV values after listening to sad music were found to vary based on gender, with men exhibiting LF and LF/HF scores higher than those of women. This observation suggests that men had higher levels of sympathetic activation and were thus affected more by sad music. The participants’ straight coding results for the number sequence did not vary between the time frame before listening to music, and the time frame after listening sad as well as cheerful music. However, variation between reverse coding scores before listening to music and after listening sad as well as cheerful music were observed with the number sequences. The reverse coding scores regarding the number sequence before listening to music were found to be lower than the comparable scores after listening sad as well as cheerful music. All these findings suggest that both cheerful and sad music samples have certain positive effects on short term memory performance.