Neuroblastic Tumor (NT) is one of the most commonly occurring tumors in children. Of all types of NTs, neuroblastoma
is the most malignant tumor that can be further categorized into undifferentiated (UD), poorly-differentiated (PD) and
differentiating (D) types, in terms of the grade of pathological differentiation. Currently, pathologists determine the
grade of differentiation by visual examinations of tissue samples under the microscope. However, this process is
subjective and, hence, may lead to intra- and inter-reader variability. In this paper, we propose a multi-resolution image
analysis system that helps pathologists classify tissue samples according to their grades of differentiation. The inputs to
this system are color images of haematoxylin and eosin (H&E) stained tissue samples. The complete image analysis
system has five stages: segmentation, feature construction, feature extraction, classification and confidence evaluation.
Due to the large number of input images, both parallel processing and multi-resolution analysis were carried out to
reduce the execution time of the algorithm. Our training dataset consists of 387 images tiles of size 512x512 in pixels
from three whole-slide images. We tested the developed system with an independent set of 24 whole-slide images, eight
from each grade. The developed system has an accuracy of 83.3% in correctly identifying the grade of differentiation,
and it takes about two hours, on average, to process each whole slide image.