Otologic surgery is performed for a variety of reasons including treatment of recurrent ear infections, alleviation of
dizziness, and restoration of hearing loss. A typical ear surgery consists of a tympanomastoidectomy in which both the
middle ear is explored via a tympanic membrane flap and the bone behind the ear is removed via mastoidectomy to treat
disease and/or provide additional access. The mastoid dissection is performed using a high-speed drill to excavate bone
based on a pre-operative CT scan. Intraoperatively, the surface of the mastoid component of the temporal bone provides
visual feedback allowing the surgeon to guide their dissection. Dissection begins in "safe areas" which, based on surface
topography, are believed to be correlated with greatest distance from surface to vital anatomy thus decreasing the chance
of injury to the brain, large blood vessels (e.g. the internal jugular vein and internal carotid artery), the inner ear, and the
facial nerve. "Safe areas" have been identified based on surgical experience with no identifiable studies showing
correlation of the surface with subsurface anatomy. The purpose of our study was to investigate whether such a
correlation exists. Through a three-step registration process, we defined a correspondence between each of twenty five
clinically-applicable temporal bone CT scans of patients and an atlas and explored displacement and angular differences
of surface topography and depth of critical structures from the surface of the skull. The results of this study reflect
current knowledge of osteogenesis and anatomy. Based on two features (distance and angular difference), two regions
(suprahelical and posterior) of the temporal bone show the least variability between surface and subsurface anatomy.
Dynamic structural and functional remodeling of the Central Nervous System occurs throughout the lifespan of the
organism from the molecular to the systems level. MRI offers several advantages to observe this phenomenon: it is non-invasive
and non-destructive, the contrast can be tuned to interrogate different tissue properties and imaging resolution
can range from cortical columns to whole brain networks in the same session. To measure these changes reliably,
functional maps generated over time with high resolution fMRI need to be registered accurately. This article presents a
new method for registering automatically thin cortical MR volumes that are aligned with the functional maps. These
acquisitions focus on the primary somato-sensory cortex, a region in the anterior parietal part of the brain, responsible for
fine touch and proprioception. Currently, these slabs are acquired in approximately the same orientation from acquisition
to acquisition and then registered by hand. Because they only cover a small portion of the cortex, their direct automatic
registration is difficult. To address this issue, we propose a method relying on an intermediate image, acquired with a
surface coil that covers a larger portion of the head to which the slabs can be registered. Because images acquired with
surface coils suffer from severe intensity attenuation artifact, we also propose a method to register these. The results
from data sets obtained with three squirrel monkeys show a registration accuracy of thirty micrometers.
Proc. SPIE. 7623, Medical Imaging 2010: Image Processing
KEYWORDS: Image processing algorithms and systems, Thalamus, Detection and tracking algorithms, Magnetic resonance imaging, Image segmentation, Image processing, Image registration, Medical imaging, Algorithm development, RGB color model
Two popular segmentation methods used today are atlas based and graph cut based segmentation techniques. The atlas
based method deforms a manually segmented image onto a target image, resulting in an automatic segmentation. The
graph cut segmentation method utilizes the graph cut paradigm by treating image segmentation as a max-flow problem.
A specialized form of this algorithm was developed by Lecoeur et al , called the spectral graph cut algorithm. The
goal of this paper is to combine both of these methods, creating a more stable atlas based segmentation algorithm that is
less sensitive to the initial manual segmentation. The registration algorithm is used to automate and initialize the spectral
graph cut algorithm as well as add needed spatial information, while the spectral graph cut algorithm is used to increase
the robustness of the atlas method. To calculate the sensitivity of the algorithms, the initial manual segmentation of the
atlas was both dilated and eroded 2 mm and the segmentation results were calculated. Results show that the atlas based
segmentation segments the thalamus well with an average Dice Similarity Coefficient (DSC) of 0.87. The spectral graph
cut method shows similar results with an average DSC measure of 0.88, with no statistical difference between the two
methods. The atlas based method's DSC value, however, was reduced to 0.76 and 0.67 when dilated and eroded
respectively, while the combined method retained a DSC value of 0.81 and 0.74, with a statistical difference found
between the two methods.
A new segmentation framework is presented taking advantage of multimodal image signature of the different brain tissues (healthy and/or pathological). This is achieved by merging three different modalities of gray-level MRI sequences into a single RGB-like MRI, hence creating a unique 3-dimensional signature for each tissue by utilising the complementary information of each MRI sequence.
Using the scale-space spectral gradient operator, we can obtain a spatial gradient robust to intensity inhomogeneity. Even though it is based on psycho-visual color theory, it can be very efficiently applied to the RGB colored images. More over, it is not influenced by the channel assigment of each MRI.
Its optimisation by the graph cuts paradigm provides a powerful and accurate tool to segment either healthy or pathological tissues in a short time (average time about ninety seconds for a brain-tissues classification).
As it is a semi-automatic method, we run experiments to quantify the amount of seeds needed to perform a correct segmentation (dice similarity score above 0.85). Depending on the different sets of MRI sequences used, this amount of seeds (expressed as a relative number in pourcentage of the number of voxels of the ground truth) is between 6 to 16%.
We tested this algorithm on brainweb for validation purpose (healthy tissue classification and MS lesions segmentation) and also on clinical data for tumours and MS lesions dectection and tissues classification.