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 highcapacity 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.