# Hyperplanar Morphological Clustering of a Hippocampus by Using Volumetric Computerized Tomography in Early Alzheimer’s Disease

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## Abstract

**:**

**Background**: On diagnosing Alzheimer’s disease (AD), most existing imaging-based schemes have relied on analyzing the hippocampus and its peripheral structures. Recent studies have confirmed that volumetric variations are one of the primary indicators in differentiating symptomatic AD from healthy aging. In this study, we focused on deriving discriminative shape-based parameters that could effectively identify early AD from volumetric computerized tomography (VCT) delineation, which was previously almost intangible.

**Methods**: Participants were 63 volunteers of Thai nationality, whose ages were between 40 and 90 years old. Thirty subjects (age 68.51 ± 5.5) were diagnosed with early AD, by using Diagnostic and Statistical Manual of Mental Disorders IV (DSM-IV) criteria and the National Institute of Neurological and Communicative Disorders and the Stroke and the Alzheimer’s disease and Related Disorders Association (NINCDS-ADRDA) criteria, while the remaining 33 were in the healthy control group (age 67.93 ± 5.5). The structural imaging study was conducted by using VCT. Three uninformed readers were asked to draw left and right hippocampal outlines on a coronal section. The resultant shapes were aligned and then analyzed with statistical shape analysis to obtain the first few dominant variational parameters, residing in hyperplanes. A supervised machine learning, i.e., support vector machine (SVM) was then employed to elucidate the proposed scheme.

**Results**: Provided trivial delineations, relatively as low as 5 to 7 implicit model parameters could be extracted and used as discriminants. Clinical verification showed that the model could differentiate early AD from aging, with high sensitivity, specificity, accuracy and F-measure of 0.970, 0.968, 0.983 and 0.983, respectively, with no apparent effect of left-right asymmetry. Thanks to a less laborious task required, yet high discriminating capability, the proposed scheme is expected to be applicable in a typical clinical setting, equipped with only a moderate-specs VCT.

## 1. Introduction

## 2. Methods

#### 2.1. Subjects

#### 2.2. Imaging Protocol

#### 2.3. SSA Software

**x**is a shape vector whose index was i = 1 … N, $\overline{x}$ is the mean shape,

**P**

_{S}and

**b**

_{S}are the 2N Eigen bases 2N dimensions and shape parameters vector of 2N elements, respectively.

**P**

_{S}, is hereon referred to as Statistical Shape (SS). It would be shown later in the experiments that due to favorable properties of the PCA, any incremental contribution due to an additional basis decreased as a more number of modes were included. In other word, majority (2–3 times the range of multi-dimensional standard deviations) of statistical distribution could be sufficiently captured by the first few modes of variations. Accordingly, a hippocampal instance may be synthesized faithfully (i.e., conforming to the hippocampal anatomy) by as few (M) truncated model parameters, as given by Equation (3), where ${\mathit{P}}_{s}^{m}$ is the mth bases vector in the shape bases and ${b}_{s}^{m}$ is the corresponding shape parameter, respectively. The number of modes M was computed such that accumulated variance up to the value M comprised required variation (e.g., within ±3σ) found in the training set.

#### 2.4. Supervised Model Clustering

#### 2.5. Benchmarking with Conventional Metric Analysis

#### 2.6. Validation and Performance Analysis

## 3. Results

#### 3.1. Hippocampal Morphology Variability

#### 3.2. Multi-Dimensional SVM Classification

#### 3.3. Optimal Number of Model Parameters

- (1)
- Both seen and unseen samples can be described by a linear combination of only 10 bases (compactness and generalization abilities of the model).
- (2)
- Any linear combination generated by randomized (with a Gaussian distribution) model parameters can synthesize a sample that is closely resemble to those previously seen (specificity) [31].

## 4. Discussion

## 5. Conclusions

## Acknowledgments

## Author Contributions

## Conflicts of Interest

## References

- Killin, L.O.J.; Starr, J.M.; Shiue, I.J.; Russ, T.C. Environmental risk factors for dementia: A systematic review. BMC Geriatr.
**2016**. [Google Scholar] [CrossRef] [PubMed] - Povova, J.; Ambroz, P.; Bar, M.; Pavukova, V.; Sery, O.; Tomaskova, H.; Janout, V. Epidemiological of and risk factors for Alzheimer’s disease: A review. Biomed. Pap. Med. Fac. Univ. Palacky Olomouc Czech Repub.
**2012**, 156, 108–114. [Google Scholar] [CrossRef] [PubMed] - Nordberg, A. Dementia in 2014: Towards early diagnosis in Alzheimer’s disease. Nat. Rev. Neurol.
**2015**, 11, 69–70. [Google Scholar] [CrossRef] [PubMed] - Haroutunian, V.; Purohit, D.P.; Perl, D.P. Neurofibrillary tangles in nondemented elder subjects and mild Alzheimer disease. Arch. Neurol.
**1999**, 56, 713–718. [Google Scholar] [CrossRef] [PubMed] - Braak, H.; Braak, E. Neuropathological staging of Alzheimer-related changes. Acta Neuropathol. (Berl.)
**1991**, 82, 239–259. [Google Scholar] [CrossRef] - Sperling, R.A.; Karlawish, J.; Johnson, K.A. Preclinical Alzheimer’s disease-the challenges ahead. Nat. Rev. Neurol.
**2013**, 9, 54–58. [Google Scholar] [CrossRef] [PubMed] - Knopman, D.S.; Boeve, B.F.; Petersen, R.C. Essentials of the proper diagnosis of mild cognitive impairment, dementia, and major subtypes of dementia. Mayo Clin. Proc.
**2003**, 78, 1290–1308. [Google Scholar] [CrossRef] [PubMed] - Alzheimer’s Association. Alzheimer’s disease facts and figures. Alzheimers Dement.
**2015**, 11, 332–384. [Google Scholar] - Dubois, B.; Feldman, H.H.; Jacova, C.; DeKosky, S.T. Research criteria for the diagnosis of Alzheimer’s disease: Revising the NINCDS-ADRDA criteria. Lancet Neurol.
**2007**, 6, 734–746. [Google Scholar] [CrossRef] - Werner, P.; Karnieli-Miller, O.; Eidelman, C. Current knowledge and future directions about the disclosure of dementia: A systematic review of the first decade of the 21st Century. Alzheimers Dement.
**2013**, 9, 74–88. [Google Scholar] [CrossRef] [PubMed] - Tang-Wai, D. A quick guide for neuroimaging of commons dementias seen in clinical practice. CGS J. CME
**2012**, 2, 18–23. [Google Scholar] - Vernooij, M.W.; Smits, M. Structural neuroimaging in aging and Alzheimer’s disease. Neuroimaging Clin. N. Am.
**2012**, 22, 33–55. [Google Scholar] [CrossRef] [PubMed] - Frisoni, G.B. Structural imaging in the clinical diagnosis of Alzheimer’s disease: Problems and tools. J. Neurol. Neurosurg. Psychiatry
**2001**, 70, 711–718. [Google Scholar] [CrossRef] [PubMed] - Shear, P.K.; Sullivan, E.V.; Mathalon, D.H.; Lim, K.O.; Davis, L.F.; Yesavage, J.A. Longitudinal volumetric computed tomographic analysis of regional brain changes in normal aging and Alzheimer’s disease. Arch. Neurol.
**1995**, 52, 392–402. [Google Scholar] [CrossRef] [PubMed] - Csernansky, J.G.; Wang, L.; Swank, J.; Miller, J.P.; Gado, M.; McKeel, D.; Miller, M.I.; Morris, J.C. Preclinical detection of Alzheimer’s disease: Hippocampal shape and volume predict onset in the elderly. Neuroimage
**2005**, 25, 783–792. [Google Scholar] [CrossRef] [PubMed] - Ichimiya, Y.; Kobayashi, K.; Arai, H.; Ikeda, K.; Kosaka, K. A computed tomography study of Alzheimer’s disease by reginal volumetric and parenchymal density measurements. J. Neurol.
**1986**, 233, 164–167. [Google Scholar] [CrossRef] [PubMed] - Cootes, T.F.; Edwards, G.J.; Taylor, C.J. Active appearance models. IEEE Trans. Pattern Recognit. Mach. Intell.
**2001**, 23, 681–685. [Google Scholar] [CrossRef] - Goodall, C. Procrustes methods in the statistical analysis of shape. J. R. Stat. Soc.
**1991**, 53, 285–339. [Google Scholar] - Neumann, A.; Lorenz, C. Statistical shape model based segmentation of medical images. Comput. Med. Imaging Graph.
**1998**, 22, 133–143. [Google Scholar] [CrossRef] - Joshia, S.H.; Narra, K.L.; Philipsa, O.R.; Nuechterlein, K.H.; Asarnow, R.F.; Toga, A.W.; Woods, R.P. Statistical shape analysis of the corpus callosum in Schizophrenia. Neuroimage
**2013**, 64, 547–559. [Google Scholar] [CrossRef] [PubMed] - Mrzilkova, J.; Zach, P.; Bartos, A.; Bartoš, A.; Tintěra, J.; Řípová, D. Volumetric analysis of the pons, cerebellum and hippocampi in patients with Alzheimer’s disease. Dement. Geriatr. Cogn. Disord.
**2012**, 34, 224–234. [Google Scholar] [CrossRef] [PubMed] - Wang, L.; Swank, J.S.; Glick, I.E.; Gado, M.H.; Miller, M.I.; Morris, J.C.; Csernansky, J.G. Changes in hippocampal volume and shape across time distinguish dementia of the Alzheimer type from healthy aging. Neuroimage
**2003**, 20, 667–682. [Google Scholar] [CrossRef] - Scher, A.I.; Xu, Y.; Korf, E.S.C.; White, L.R.; Scheltens, P.; Toga, A.W.; Thompson, P.M.; Hartley, S.W.; Witter, M.P.; Valentino, D.J.; et al. Hippocampal shape analysis in Alzheimer’s disease: A population-based study. Neuroimage
**2007**, 36, 8–18. [Google Scholar] [CrossRef] [PubMed] - American Psychiatric Association, Task Force on DSM-IV. Diagnostic and Statistical Manual of Mental Disorders DSM-IV-TR, 4th ed.; American Psychiatric Association: Washington, DC, USA, 2004; pp. 78–94. [Google Scholar]
- McKhann, G.M.; Knopman, D.S.; Chertkow, H.; Hyman, B.T.; Jack, C.R.; Kawas, C.H.; Klunk, W.E.; Koroshetz, W.J.; Manly, J.J.; Mayeux, R.; et al. The diagnosis of dementia due to Alzheimer’s disease: Recommendations from the National Institute on Aging and the Alzheimer’s Association workgroup. Alzheimers Dement.
**2011**, 7, 263–269. [Google Scholar] [CrossRef] [PubMed] - Wang, H.; Kearney, J.; Kendall, A. Arc-Length Parameterized Spline Curves for Real-Time Simulation; Curve and Surface Design: Saint-Malo, France, 2002; pp. 387–396. [Google Scholar]
- Gower, J.C.; Dijksterhuis, G.B. Procrustes Problems; Oxford University Press: Oxford, UK, 2004; p. 248. [Google Scholar]
- Vapnik, V. Statistical Learning Theory; Springer: New York, NY, USA, 1998. [Google Scholar]
- Chang, C.C.; Lin, C.J. Libsvm: A library for support vector machines. ACM Trans. Intell. Sys. Technol.
**2011**, 2, 27. [Google Scholar] [CrossRef] - Shen, K.K.; Fripp, J.; Mériaudeau, F.; Salvado, O. Detecting global and local hippocampal shape changes in Alzheimer’s disease using statistical shape models. Neuroimage
**2012**, 59, 2155–2166. [Google Scholar] [CrossRef] [PubMed] - Horkaew, P.; Yang, G.Z. Construction of 3D Dynamic Statistical Deformable Models for Complex Topological Shapes. In Proceedings of the Medical Image Computing and Computer Assisted Intervention (MICCAI), Saint-Malo, France, 26–29 September 2004. [Google Scholar]
- Thorburn, W.M. The Myth of Occam’s razor. Mind
**1918**, 27, 345–353. [Google Scholar] [CrossRef] - Dyakin, V.V.; Lucas, J.; Dyakina-Fagnano, N.V.; Posner, E.V.; Vadasz, C. Chain of Chirality Transfer as Determinant of Brain Functional Laterality. Breaking the Chirality Silence: Search for New Generation of Biomarkers. Relevance to Neurodegenerative Diseases, Cognitive Psychology and Nutrition Science. Neurol. Neurosci. Res.
**2017**. [Google Scholar] [CrossRef] - Goncalves-Pereira, P.M.; Oliveira, E.; Insausti, R. Quantitative volumetric analysis of the hippocampus, amygdala and entorhinal cortex: Normative database for the adult Portuguese population. Rev. Neurol.
**2006**, 42, 713–722. [Google Scholar] [PubMed] - Woolard, A.A.; Heckers, S. Anatomical and functional correlates of human hippocampal volume asymmetry. Psychiatry Res.
**2012**, 201, 48–53. [Google Scholar] [CrossRef] [PubMed]

**Figure 2.**Annotated sketch (

**left**) (A = Hippocampal formation height, B = Hippocampal and stem distance, C = Choroid fissure width, D = Temporal horn width) of topological prescription and the corresponding shape overlaid on an image (

**right**). For clarity, the fiducial markers (stars) are placed on the left contour, while the regularized control points (circles) are shown on the right one.

**Figure 5.**Scatter plots of two model parameters extracted from 382 hippocampi. Horizontal and vertical axes represents the first and second modes of variations, respectively.

**Figure 7.**Histograms showing normalized area distribution (

**top**) and corresponding approximated Gaussian curves (

**bottom**) of left and right hippocampi for both control and subjects. Black arrows indicate overlapping area.

**Figure 8.**The overall trend of clustering performance versus the model complexity. The arrows indicate the optimal number of model parameters for diagnosing early AD.

Subject N = 33 | Control N = 30 | p | |
---|---|---|---|

Age (40–90 Year) | 68.51 ± 5.5 | 67.93 ± 5 | 0.076 |

Female | 25 (75.8%) | 15 (50%) | 0.065 |

Highest level of education | Less than Level 6 (90.9%) | Less than Level 6 (60%) | 0.034 |

Occupation | Retired (75.8%) | Retired (50%) | 0.066 |

Family history of dementia | None | None | - |

Average blood pressure (mmHg) | 135.1/75.5 ± 14.2/7.3 | 139.2/81.4 ± 14.4/8 | 0.064 |

TMSE* score (point) | 18.3 ± 1.6 | 27.5 ± 1.6 | 0.027 |

Attributes | Left | % | Right | % |
---|---|---|---|---|

Correctly Classified Instances | 60 | 95.2381 | 62 | 98.4127 |

Incorrectly Classified Instance | 3 | 4.7619 | 1 | 1.5873 |

Kappa Statistics | 0.9047 | 0.9682 | ||

Mean Absolute Error | 0.0476 | 0.0159 | ||

Root Mean Squared Error | 0.2182 | 0.1260 | ||

Relative Absolute Error | 9.5395% | 3.1798% | ||

Root Relative Squared Error | 43.6690% | 25.2123% |

**Table 3.**Numbers of correctly (TP–C, TP–S, TN–C and TN–S) and incorrectly (FP–C, FP–S, FN–C and FN–S) classified hippocampal samples w.r.t. number of model parameters (modes) taken into account.

Left | Correctly Classified (Samples) | Incorrectly Classified (Samples) | ||||||

Modes | TP–C | TP–S | TN–C | TN–S | FP–C | FP–S | FN–C | FN–S |

1 | 29 | 30 | 29 | 30 | 3 | 1 | 1 | 3 |

2 | 28 | 31 | 28 | 31 | 2 | 2 | 2 | 2 |

3 | 29 | 31 | 29 | 31 | 2 | 1 | 1 | 2 |

4 | 29 | 30 | 29 | 30 | 3 | 1 | 1 | 3 |

5 | 29 | 31 | 29 | 31 | 2 | 1 | 1 | 2 |

6 | 29 | 32 | 29 | 32 | 1 | 1 | 1 | 1 |

7 | 29 | 33 | 29 | 33 | 0 | 1 | 1 | 0 |

8 | 29 | 33 | 29 | 33 | 0 | 1 | 1 | 0 |

9 | 29 | 33 | 29 | 33 | 0 | 1 | 1 | 0 |

10 | 29 | 33 | 29 | 33 | 0 | 1 | 1 | 0 |

Right | Correctly Classified (Samples) | Incorrectly Classified (Samples) | ||||||

Modes | TP-C | TP-S | TN-C | TN-S | FP-C | FP-S | FN-C | FN-S |

1 | 30 | 32 | 30 | 32 | 1 | 0 | 0 | 1 |

2 | 29 | 32 | 29 | 32 | 1 | 1 | 1 | 1 |

3 | 30 | 32 | 30 | 32 | 1 | 0 | 0 | 1 |

4 | 30 | 32 | 30 | 32 | 1 | 0 | 0 | 1 |

5 | 30 | 33 | 30 | 33 | 0 | 0 | 0 | 0 |

6 | 30 | 33 | 30 | 33 | 0 | 0 | 0 | 0 |

7 | 30 | 33 | 30 | 33 | 0 | 0 | 0 | 0 |

8 | 30 | 33 | 30 | 33 | 0 | 0 | 0 | 0 |

9 | 30 | 33 | 30 | 33 | 0 | 0 | 0 | 0 |

10 | 30 | 32 | 30 | 32 | 1 | 0 | 0 | 1 |

**Table 4.**Sensitivity, specificity, precision, accuracy and F-measure for the controls (C) and subjects (S) groups. The results w.r.t. the number of modes (M) of both left (L) and right (R) sides are shown.

L | Sensitivity | Specificity | Precision | Accuracy | F-Measure | |||||

M | C | S | C | S | C | S | C | S | C | S |

1 | 0.967 | 0.909 | 0.906 | 0.968 | 0.906 | 0.968 | 0.935 | 0.938 | 0.935 | 0.938 |

2 | 0.933 | 0.939 | 0.933 | 0.939 | 0.933 | 0.939 | 0.933 | 0.939 | 0.933 | 0.939 |

3 | 0.967 | 0.939 | 0.935 | 0.969 | 0.935 | 0.969 | 0.951 | 0.954 | 0.951 | 0.954 |

4 | 0.967 | 0.909 | 0.906 | 0.968 | 0.906 | 0.968 | 0.935 | 0.938 | 0.935 | 0.938 |

5 | 0.967 | 0.939 | 0.935 | 0.969 | 0.935 | 0.969 | 0.951 | 0.954 | 0.951 | 0.954 |

6 | 0.967 | 0.970 | 0.967 | 0.970 | 0.967 | 0.970 | 0.967 | 0.970 | 0.967 | 0.970 |

7 | 0.967 | 1 | 1 | 0.971 | 1 | 0.971 | 0.983 | 0.985 | 0.983 | 0.985 |

8 | 0.967 | 1 | 1 | 0.971 | 1 | 0.971 | 0.983 | 0.985 | 0.983 | 0.985 |

9 | 0.967 | 1 | 1 | 0.971 | 1 | 0.971 | 0.983 | 0.985 | 0.983 | 0.985 |

10 | 0.967 | 1 | 1 | 0.971 | 1 | 0.971 | 0.983 | 0.985 | 0.983 | 0.985 |

R | Sensitivity | Specificity | Precision | Accuracy | F-Measure | |||||

M | C | S | C | S | C | S | C | S | C | S |

1 | 1 | 0.970 | 0.968 | 1 | 0.968 | 1 | 0.984 | 0.985 | 0.984 | 0.985 |

2 | 0.967 | 0.970 | 0.967 | 0.970 | 0.967 | 0.970 | 0.967 | 0.970 | 0.967 | 0.970 |

3 | 1 | 0.970 | 0.968 | 1 | 0.968 | 1 | 0.984 | 0.985 | 0.984 | 0.985 |

4 | 1 | 0.970 | 0.968 | 1 | 0.968 | 1 | 0.984 | 0.985 | 0.984 | 0.985 |

5 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |

6 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |

7 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |

8 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |

9 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |

10 | 1 | 0.970 | 0.968 | 1 | 0.968 | 1 | 0.984 | 0.985 | 0.984 | 0.985 |

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**MDPI and ACS Style**

Suksuphew, S.; Horkaew, P. Hyperplanar Morphological Clustering of a Hippocampus by Using Volumetric Computerized Tomography in Early Alzheimer’s Disease. *Brain Sci.* **2017**, *7*, 155.
https://doi.org/10.3390/brainsci7110155

**AMA Style**

Suksuphew S, Horkaew P. Hyperplanar Morphological Clustering of a Hippocampus by Using Volumetric Computerized Tomography in Early Alzheimer’s Disease. *Brain Sciences*. 2017; 7(11):155.
https://doi.org/10.3390/brainsci7110155

**Chicago/Turabian Style**

Suksuphew, Sarawut, and Paramate Horkaew. 2017. "Hyperplanar Morphological Clustering of a Hippocampus by Using Volumetric Computerized Tomography in Early Alzheimer’s Disease" *Brain Sciences* 7, no. 11: 155.
https://doi.org/10.3390/brainsci7110155