=Paper=
{{Paper
|id=Vol-2210/paper14
|storemode=property
|title=Hippocampus detection and calculation of its characteristics in magnetic resonance imaging of the brain
|pdfUrl=https://ceur-ws.org/Vol-2210/paper14.pdf
|volume=Vol-2210
|authors=Vladimir Gridin,Maxim Truphanov,Vladimir Solodovnikov
}}
==Hippocampus detection and calculation of its characteristics in magnetic resonance imaging of the brain==
https://ceur-ws.org/Vol-2210/paper14.pdf
Hippocampus detection and calculation of its characteristics
in magnetic resonance imaging of the brain
V N Gridin1, M I Truphanov1 and V I Solodovnikov 1
1
Center of Information Technologies in Engineering RAS, Marshal Biryuzov Str. 7а,
Odintsovo, Moscow region, Russia, 143000
Abstract. Hippocampus is the most informative object of the brain for the purpose of detection
of Alzheimer's disease signs. This paper presents the approach which was developed for
detection of hippocampus and subsequent calculation of its parameters while analyzing the
series of images obtained in a sagittal projection by means of a magnetic resonance
tomography. This paper introduces the algorithms for detecting the keyframes in the entire
series which contain the hippocampus and for identifying the hippocampus among other brain
structures. The problems of measuring the volumatric parameters of the hippocampus and
calculating its characteristics are considered. These characteristics serve as the basis for
instrumental calculation of the signs which characterize the possible presence of Alzheimer's
disease.
1. Introduction
The increased life expectancy of the world's population results in the increase of the occurrence and
prevalence of Alzheimer's disease. According to the World Health Organization, the number of people
suffering from Alzheimer's disease will double every twenty years and will reach 115.4 million by
2050. Regarding this issue, it is critical to solve the task of identifying the disease at the earliest stage
before the cognitive impairments affect the daily activity of a person, while there is still the potential
possibility to slow down the progression of thedisease.
Unfortunately, there is no harmless, inexpensive, and most importantly, non-invasive methodto
diagnose Alzheimer's disease with a high degree of certainty. Currently,confirmation of the final
diagnosis includes a histopathological analysis of brain tissue or the cerebrospinal fluid study in order
to determine the formation of beta-amyloid, the characteristic of Alzheimer's disease, for which the
patient is required to undergo a spinal cord puncture. Also, there is a method of positron emission
tomography of amyloid based on the use of radioactive material, which is a technologically complex
and expensive process and implies the effect of radioactive radiation on the examined person. In this
case, particular importance is acquired by the tasks related to development of the automated methods,
algorithms, software and hardware to detect abnormalities in the structure and activity of the brain
which are based on the visual analysis of optical cross-sections of the head in various planes obtained
with the help of a magnetic resonance tomography (MRI).
The extensive range of works is devoted to the problems of the construction of the automated
system for MRI images processing aimed to analyze the morphological features, which are inherent to
Alzheimer's disease. Most of these works note that, first of all, the changes affect the temporal region
of the brain and especially hippocampus. Thus, the primary and essential tasks are the detection of
IV International Conference on "Information Technology and Nanotechnology" (ITNT-2018)
Image Processing and Earth Remote Sensing
V N Gridin, M I Truphanov and V I Solodovnikov
hippocampus and calculation of its volumetric parameters for the purpose of the automated diagnosis
of Alzheimer's disease [1, 2].
Currently, various software products (both commercial and freeware) are known that are used to
automate the measurement process and calculate the characteristics of brain structures, in particular,
the hippocampus and adjacent areas [3,4]. Their common drawback is the mandatory use of some
manual operations which complicates the analysis process and leads to the errors in measurement of
the key characteristics. The consequence is the potential errors in the diagnosis.
Thus, in [5] area measurements of the hippocampal region of rats brain were performed by means
of analysis of the images with the help of a specialized software tool, focusing on the "Rat Brain"
atlas, Paxinos G. and Watson C. The authors have used this atlas to find pre-known positions of the
hippocampus in the general structure the brain. Obviously, an approach based on the use of atlas can
be applied only for reading the estimated coordinates of the hippocampal region. It requires
considerable refinement for the purpose of detection and measurement of the linear and volumetric
parameters of the hippocampus.
In [6], the following digital image processing operations were applied providing the solution of a
part of the problem in automatic mode: subtraction of the background to minimize the influence of the
background component; the transformation of the image into monochrome; increasing of contrast;
binarization; noise suppression. However, it is indicated that these operations were performed in
manual mode using specialized software. The results of using this approach present a certain interest
in terms of determining the certain parameters of individual steps during automated visual data
processing.
The key features of the hippocampus structure are specified in [7, 8]. The authors in [8] carried out
the research of the hippocampus images using specialized programs in an automated mode in the
"FreeSurfer" software environment, as well as in semi-automatic and manual modes in the "Display"
package. This software allows a user to perform operations on allocation of the brain structures and
their volumetric evaluation. Standard operations of digital image processing (contour highlighting,
binarization, etc.) were used to obtain the results. However, human participation was still necessary for
the detection of the hippocampus. These approaches are not applicable for the automatic evaluation of
the volumetric and dimensional parameters of hippocampus and adjacent brain structures.
In a number of works, the analysis of statistical characteristics and texture analysis as well as
artificial neural networks are used to highlight brain structures (including hippocampus) [9, 10, 11].
In particular, in [12] the authors showed the possibility of using texture image characteristics
together with an artificial neural network for analysis of the hippocampus region. However, a large
training sample is needed to use this approach, which complicates the practical application of this
approach. Another difficulty includes the automatic normalization which is necessity for orientation
and spatial characteristics of the three-dimensional regions formed during the analysis of textures,
which also reduces the practical value of this approach.
The issue of tomographic images analysis automation for other internal organs also becomes more
and more relevant. So in the article [13] the simplest technology of automatic recognition of
emphysema of lungs by sets of two-dimensional diagnostic images of computed tomography is
considered. In [14], a method for segmentation of organs of the retroperitoneal space on tomographic
images based on the level function was proposed.
In this connection, it can be concluded that further research is needed to improve the methodology
and mathematical apparatus for automation of magnetic resonance imaging analysis and the
calculation of hippocampus characteristics as a key informative brain structure, which would be an
essential step in the Alzheimer's disease diagnosis.
2. The key stages in processing the series of MRI images
The developed approach for the visual MRI data processing aims to create tools for constructing
automatic subsystems of analysis of brain structures images, in particular, the hippocampus for the
Alzheimer's disease diagnosis. The following main stages could be picked out:
• selection of series slices in the sagittal projection, presumably containing the hippocampus;
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• subsequent iterative processingin order to confirm /not confirm the detection of the
hippocampus in each frame;
• clarifying the location of the hippocampus and calculating its characteristics by analyzing a
series of neighboring frames or by repeating the selection of frames sequence presumably
containing the hippocampus;
• transmission of the received parameter vector to the decision support module to get
information about the possible presence of the Alzheimer's disease, the healthy state or the
presence of brain changes not caused by Alzheimer's disease.
Start
1
Entering
Images
2
Images
Normalization
3
11
Basic images selection
Pre-selection of the
containing the hippocampal
hippocampal region
area
4
Noise
Reduction Comparison 12 No matches
of the selected areas
5 with the reference
Borders 13
Allocation Changing parameters and
reselecting frames containing
6 the hippocampus
Contour lines skeletonization
on a gradient image 14
Specification of the
hippocampus region and
contour
7
Contour 15
Coding Calculation of the
hippocampal parameters
8 16
Statistical data processing
Are all circuits (analysis of changes
closed? dynamics, including the
9
neural network approach)
Contours Tracing
Gaps Recovering
10 End
Texture characteristics
calculation within the found
areas
Figure 1. A generalized algorithm for hippocampus detecting and measuring of its parameters for the
Alzheimer's disease signs detection.
Let’s consider the first group of operations aimed at the slice search and selection in the MRI
sequence containing the hippocampus. Note that here and further we consider the location of the
hippocampus in the left and right hemispheres of the brain. The main idea is to sequentially scan
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V N Gridin, M I Truphanov and V I Solodovnikov
images in the saggital projection and search for a structure that is supposedly appropriate to the
hippocampus shape and location. The location is clarified in relation to the location of the eye, which
is very informative since it is characterized by a unique close to round shape and as having a closed
contour. Also, the calculations of the brain eccentricity and the neck vessels eccentricity are used to
determine the orientation in space and the boundaries of the brain as the closed objects which have the
largest area. The brain boundaries are calculated by detecting several curves of identical shape and
length, located close to each other. These curves are the boundaries of the cranium and intracranial
fluid.
The generalized algorithm of the proposed approach is shown in Figure 1.
3. The key stages for the series of MRI images processing
The hippocampus could be detected in a series of consecutive frames, on which there is an elongated
body framed by a liquid, characterized by a closed contour of a pre-known shape and with pre-
computed textural characteristics.
Figure 2 presents images, which illustrate the process of primary detection of the hippocampus in a
series of sagittal MRI images.
Detection of multiple
identical in form lines
The initial frame
Eye area detection containing the
supposedly hippocampus Closed object,
corresponding to the A closed object,
characteristics of the eye presumably containing a
hippocampus
Detection of the
maxillary sinus area
a) b)
Hippocampal area and
surroundings Hippocampal area
Eye area confirmation detection confirmation
Confirming the maxillary
sinus area
c) d)
Figure 2. The process of search of the hippocampus: a, b) - the initial frames presumably containing
the hippocampus; c, d) the confirmed frames containing the hippocampus in the middle of the
sequence.
Thus, the search in the general sequence of sagittal images is directly related to the images
detection on which the eyes and maxillary sinuses could be detected, as the most informative and
precisely localized objects. Then, the skull boundary and the brain boundary are determined on the
found sections to establish the relative position of the hippocampus and to calculate its potential
location. Next, by analyzing a series of successive frames, it is iteratively determined those frames on
which there is a region corresponding to a generalized description of the brightness and spatial
characteristics of the hippocampus. The confirmation of the detection is made at the final stage by
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V N Gridin, M I Truphanov and V I Solodovnikov
constructing a closed contour for boundaries description. In addition, the comparison with the
reference values and parameters of the adjacent hippocampus regions takes place.
If the hippocampus detection was not confirmed, the initial frame of the sequence is selected again
and the search process has to be repeated with the changed parameter values.
4. Determination of the hippocampus boundaries and position
Let's describe the process of precise determination of the hippocampus boundaries and position in
more details. This process consists of the following main steps:
• pre-processing - reducing the random noise level by a Gaussian filter and adjusting the
contrast to the reference parameters [15];
• locating the objects boundaries in the image by means of a differential operator;
• binarizationof the obtained boundaries by the threshold operator with adaptive threshold
calculation based on the Otsu method;
• skeletonizationof the obtained contours and restoration of discontinuities in contours with the
help of the initial halftone image analysis;
• additional confirmation of the detected hippocampus on the basis of generalized spectral
characteristics of local image areas;
• inspecting the series of frames to determine the size of the hippocampal regions relative to the
total brain size, and calculating the dimensions of the fluid, which adjacent to the
hippocampus.
The key steps of processing each MRI image frame are shown in Figure 3.
a) – contrast change b) – image area differentiation
c) – contours binarization and skeletonization d) – spectral parameters calculation
Figure 3. The process of the hippocampus detection confirmation and its characteristics
determination.
5. Noise elimination
Noise elimination consists of smoothing and eliminating of sudden jumps of brightness associated
with the physical process of magnetic resonance imaging and the intrinsic noise of the tract of
tomograph.
The filtering process is based on the calculation of the new luminance value g with a help of the
Gaussian filter for each discrete image point according to the formula:
1 d2
−
g(x, y) = e 2σ2 ,
√2πσ
where σ is the filter parameter, d = √(x − xc )2 + (y − yc )2 is the distance of the [x,y] pixel from the
center pixel of the neighborhood [xc , yc ] , which determines the radius of the filter.
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6. Selection of contours
The gradient vector of the image brightness function is calculated to extract the contours at each point.
This gradient vector is described by the module g ( x, y) and the direction. The set of gradient vectors
at each point of the filtered image f '(x,y) is a gradient image [16]
Gr g ( x, y), v( x, y) , x 1, K , y 1, N
,
containing information about the differences in the brightness function, i.e. about contour lines (K, N
are the image dimensions).
Calculation of the hippocampus and adjacent areas parameters is performed on a series of adjacent
frames and the contour image shown in Figure 4.
Figure 4. Contour image which is used to calculate hippocampal parameters.
It is worth noting that the obtained absolute values characterizing the state of the hippocampus, in
the future, have to be transformed into relative values taking into account the total volume of the brain
and the patient's gray matter. Also, all changes in the form of the hippocampus itself and of the
surrounding areas could play an important role in the diagnosis. These changes, in turn, could be
revealed by the resulting contour images.
7. Conclusion
The analysis of scientific, patent sources and reference information revealed that there is a lack of a
developed algorithmic and software-hardware solution for automatic analysis of MRI data in order to
detect abnormalities in the structure and activity of the brain inherent to Alzheimer's disease at an
early stage. The task of the automatically hippocampus detection on MRI images and selection of the
most informative images in a general sequence is the key task while constructing both automatic and
automated means of calculating signs, which characteristic the Alzheimer's disease. This paper
suggests an approach for automatic detection and measurement of the spatial characteristics of the
hippocampus, as well as the allocation of nearby areas. This information is used as a characteristic
space for making a decision about the possible presence of the disease. The practical significance of
the being developed approach lies in the subsequent construction of a specialized domestic software
product that allows automatic and automated analysis of the brain MRI images in the interests of
timely detection of Alzheimer's disease and instrumental evaluation of its progress dynamics.
8. References
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Acknowledgments
The study was carried out at the expense of a grant from the Russian Science Foundation (project 17-
11-01288).
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