Quantitative measurements of tumor response rate in three dimensions (3D) become more re-alistic with the use of advanced technology im-aging during therapy, especially when the tumor morphological changes remain subtle, irregular and difficult to assess by clinical examination. These quantitative measurements depend strongly on the accuracy of the segmentations methods used. Improvements on such methods yield to increase the accuracy of the segmentation process. Recently, the essential modification in the Traditional Region Growing (T-RG) method has been developed and a “Modified Region Growing Method” (MRGM) has been presented and gives more accurate boundary detection and holes filling after segmentation. In this pa-per, the new automatic calculation of the volu-metric size of brain tumor has been imple-mented based on Modified Region Growing Method. A comparative study and statistical analysis performed in this work show that the modified method gives more accurate and better performance for 3D volume measurements. The method was tested by 7 fully investigated pa-tients of different tumor type and shape, and better accurate results were reported using MRGM

Quantitative measurements of tumor volume becomes more realistic with the use of imaging- particularly specially when the tumor have non-ellipsoidal morphology, which remains subtle, irregular and difficult to assess by visual metric and clinical examination. The quantitative measurements depend strongly on the accuracy of the segmentation technique. The validity of brain tumor segmentation methods is an important issue in medical imaging because it has a direct impact on many applications such as surgical planning and quantitative measurements of tumor volume. Our goal was to examine two popular segmentation techniques seeded region growing and active contour "snakes" to be compared against experts' manual segmentations as the gold standard. We illustrated these methods on brain tumor volume cases using MR imaging modality.

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When analyzing ultrasound images, the processed data depends strongly on the settings of the equipment. So, the overall gain, Time-Gain-Compensation(TGC) , Diffraction and Focusing, pre-and post processing of the gray levels, all play a role in the estimation of the texture parameters. To correct for these dependencies, we used images from tissue mimicking phantom with the same settings of the ultrasound equipment as during the clinical procedure. The acoustic properties of the phantom have been estimated in the device developed for acoustic microscopy.