Segmenting tumor from MRI images is an essential but time consuming manual duty. Performing an automatic segmentation is a defying task since different forms of tumor tissue exist for diverse patients and in many cases the tumor is similar to the normal tissue. Various studies proposed earlier to handle the issue of precisely segmenting the tumor but they discard the degradations and their effect to the precision of the segmentation. This article provides a more precise segmentation process through the use of appropriate pre-processing algorithms. The authors studied many enhancement and restoration algorithms and selected the NL-means, Laplacian filter and histogram equalization to be used as preprocessing techniques. Experimental results showed that using a suitable preprocessing scheme would produce a better segmentation process.

Observed images with bare eyes are always different than the acquired ones using an imaging system since the captured images are considered as the degraded versions of the original scene. These degradations may vary between image noise, lighting defects and blur. Therefore, this article addresses the field of computer forensics with image deblurring as the latent details that are indeed present in the captured images are concealed due to the blurring artifact. Moreover, the constant types of blur that are being dealt with in forensics are the motion and the out-of-focus blur. The motion blur occurs due to the motion of the recorded objects or the camera during the capturing process. The out-of-focus blur occurs due to lens defocusing errors. Different examples are provided to focus on the importance of deblurring forensic images. In addition, concise commentaries on deblurring methods, applications and blur types are deliberated for additional knowledge.