1. An automated method for detecting a disease state, the method comprising: identifying a bone surface in each of one or more image data sets using a bone surface identifying module, wherein the one or more image data sets correspond to a region of interest in an object of interest, and wherein identifying the bone surface in each of the one or more image data sets comprises: obtaining a first image corresponding to the region of interest in a normal subject; obtaining a second image corresponding to the region of interest in the object of interest; identifying an acoustic shadow in the second image; generating a mask based on the identified acoustic shadow; segmenting a joint capsule region corresponding to each of the one or more image data sets based on a corresponding identified bone surface using a joint capsule segmenting module, wherein segmenting the joint capsule region comprises: developing an atlas of the region of interest; determining a point in the atlas that is disposed at a location farthest away from a determined position; identifying one or more points in the atlas where one or more bone surfaces forming the region of interest start curving towards the farthest point; ascertaining an initial estimate of a location of the joint capsule region; generating a prior likelihood for the joint capsule region based on the identified bone surface; segmenting the joint capsule region based on the prior likelihood; and analyzing the segmented joint capsule region to identify the disease state using an analysis module.