Craniopharyngioma 0: A New Addition to the Tumor Classification Scheme

Object Craniopharyngiomas are notoriously difficult to treat. Surgeons must weigh the risks of aggressive resection against the long-term challenges of recurrence. Because of their parasellar location, often extending well beyond the sella, these tumors challenge vision and pituitary and hypothalamic function. New techniques are needed to improve outcomes in patients with these tumors while decreasing treatment morbidity. An endoscopic expanded endonasal approach (EEA) is one such technique that warrants understanding and evaluation. The authors explain the techniques and approach used for the endoscopic endonasal resection of suprasellar craniopharyngiomas and introduce a tumor classification scheme. Methods The techniques and approach used for the endoscopic, endonasal resection of suprasellar craniopharyngiomas is explained, including the introduction of a tumor classification scheme. This scheme is helpful for understanding both the appropriate expanded approach as well as relevant involved anatomy. Results The classification scheme divides tumors according to their suprasellar extension: Type I is preinfundibular; Type II is transinfundibular (extending into the stalk); Type III is retroinfundibular, extending behind the gland and stalk, and has 2 subdivisions (IIIa, extending into the third ventricle; and IIIb, extending into the interpeduncular cistern); and Type IV is isolated to the third ventricle and/or optic recess and is not accessible via an endonasal approach. Conclusions The endoscopic EEA requires a thorough understanding of both sinus and skull base anatomy. Moreover, in its application for craniopharyngiomas, an understanding of tumor growth and extension with respect to the optic chiasm and infundibulum is critical to safely approach the lesion via an endonasal route.

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Combining Wavelet Texture Features and Deep Neural Network for Tumor Detection and Segmentation Over MRI

Journal of Intelligent Systems ◽

10.1515/jisys-2017-0090 ◽

2019 ◽

Vol 28 (4) ◽

pp. 571-588 ◽

Cited By ~ 2

Author(s):

Srinivasalu Preethi ◽

Palaniappan Aishwarya

Keyword(s):

Brain Tumor ◽

Classification Scheme ◽

Texture Features ◽

The Body ◽

Tumor Classification ◽

Flower Pollination Algorithm ◽

Fuzzy C Means Clustering ◽

Tumor Region ◽

Occurrence Matrix ◽

The Brain

Abstract A brain tumor is one of the main reasons for death among other kinds of cancer because the brain is a very sensitive, complex, and central portion of the body. Proper and timely diagnosis can prolong the life of a person to some extent. Consequently, in this paper, we have proposed a brain tumor classification scheme on the basis of combining wavelet texture features and deep neural networks (DNNs). Normally, the system comprises four modules: (i) feature extraction, (ii) feature selection, (iii) tumor classification, and (iv) segmentation. Primarily, we eliminate the noise from the image. Then, the feature matrix is produced by combining wavelet texture features [gray-level co-occurrence matrix (GLCM)+wavelet GLCM]. Following that, we select the relevant features with the help of the oppositional flower pollination algorithm (OFPA) because a high number of features are major obstacles for classification. Then, we categorize the brain image based on the selected features using the DNN. After the classification procedure, the projected scheme extracts the tumor region from the tumor images with the help of the possibilistic fuzzy c-means clustering (PFCM) algorithm. The experimentation results show that the proposed system attains the better result associated with the available methods.

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Statistical analysis of classification

Symposium - International Astronomical Union ◽

10.1017/s0074180900053420 ◽

1966 ◽

Vol 24 ◽

pp. 188-189

Author(s):

T. J. Deeming

Keyword(s):

Multivariate Analysis ◽

Statistical Analysis ◽

Classification Scheme ◽

Analytical Procedure ◽

Narrow Band ◽

Scientific Research ◽

Maximum Amount ◽

Amount Of Information

If we make a set of measurements, such as narrow-band or multicolour photo-electric measurements, which are designed to improve a scheme of classification, and in particular if they are designed to extend the number of dimensions of classification, i.e. the number of classification parameters, then some important problems of analytical procedure arise. First, it is important not to reproduce the errors of the classification scheme which we are trying to improve. Second, when trying to extend the number of dimensions of classification we have little or nothing with which to test the validity of the new parameters.Problems similar to these have occurred in other areas of scientific research (notably psychology and education) and the branch of Statistics called Multivariate Analysis has been developed to deal with them. The techniques of this subject are largely unknown to astronomers, but, if carefully applied, they should at the very least ensure that the astronomer gets the maximum amount of information out of his data and does not waste his time looking for information which is not there. More optimistically, these techniques are potentially capable of indicating the number of classification parameters necessary and giving specific formulas for computing them, as well as pinpointing those particular measurements which are most crucial for determining the classification parameters.

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Introductory paper

Symposium - International Astronomical Union ◽

10.1017/s0074180900053031 ◽

1966 ◽

Vol 24 ◽

pp. 3-5

Author(s):

W. W. Morgan

Keyword(s):

Line Intensity ◽

Classification Scheme ◽

Two Dimensional ◽

Spectral Classification ◽

Dimensional Array ◽

Rr Lyrae ◽

Metallic Line ◽

Introductory Paper ◽

Parameter Varying ◽

Definition Of

1. The definition of “normal” stars in spectral classification changes with time; at the time of the publication of theYerkes Spectral Atlasthe term “normal” was applied to stars whose spectra could be fitted smoothly into a two-dimensional array. Thus, at that time, weak-lined spectra (RR Lyrae and HD 140283) would have been considered peculiar. At the present time we would tend to classify such spectra as “normal”—in a more complicated classification scheme which would have a parameter varying with metallic-line intensity within a specific spectral subdivision.

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A Perturbation Theory for the Population of Atomic Energy Levels in Non-Lte Plasmas

International Astronomical Union Colloquium ◽

10.1017/s025292110010805x ◽

1988 ◽

Vol 102 ◽

pp. 343-347

Author(s):

M. Klapisch

Keyword(s):

Perturbation Theory ◽

Small Parameter ◽

Classification Scheme ◽

Sum Rules ◽

Energy Levels ◽

Natural Classification ◽

Quantum Numbers ◽

Formal Expansion ◽

Atomic Energy Levels ◽

As Effects

AbstractA formal expansion of the CRM in powers of a small parameter is presented. The terms of the expansion are products of matrices. Inverses are interpreted as effects of cascades.It will be shown that this allows for the separation of the different contributions to the populations, thus providing a natural classification scheme for processes involving atoms in plasmas. Sum rules can be formulated, allowing the population of the levels, in some simple cases, to be related in a transparent way to the quantum numbers.

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The value of Electron Microscope studies of imperfect natural diamonds

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100174102 ◽

1990 ◽

Vol 48 (4) ◽

pp. 194-195

Author(s):

J C Walmsley ◽

A R Lang

Keyword(s):

Electron Microscope ◽

Classification Scheme ◽

Natural Diamond ◽

Sudden Change ◽

Growth Conditions ◽

Diamond Growth ◽

Major Constituent ◽

Natural Diamonds ◽

Type Ia ◽

Platelet Defects

Interest in the defects and impurities in natural diamond, which are found in even the most perfect stone, is driven by the fact that diamond growth occurs at a depth of over 120Km. They display characteristics associated with their origin and their journey through the mantle to the surface of the Earth. An optical classification scheme for diamond exists based largely on the presence and segregation of nitrogen. For example type Ia, which includes 98% of all natural diamonds, contain nitrogen aggregated into small non-paramagnetic clusters and usually contain sub-micrometre platelet defects on {100} planes. Numerous transmission electron microscope (TEM) studies of these platelets and associated features have been made e.g. . Some diamonds, however, contain imperfections and impurities that place them outside this main classification scheme. Two such types are described.First, coated-diamonds which possess gem quality cores enclosed by a rind that is rich in submicrometre sized mineral inclusions. The transition from core to coat is quite sharp indicating a sudden change in growth conditions, Figure 1. As part of a TEM study of the inclusions apatite has been identified as a major constituent of the impurity present in many inclusion cavities, Figure 2.

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Classification scheme for freesia. Schema de classification pour le freesia

EPPO Bulletin ◽

10.1046/j.1365-2338.2002.00544.x ◽

2002 ◽

Vol 32 (1) ◽

pp. 179-183

Keyword(s):

Classification Scheme

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Classification scheme for crocus. Schema de classification pour le crocus

EPPO Bulletin ◽

10.1046/j.1365-2338.2002.00539.x ◽

2002 ◽

Vol 32 (1) ◽

pp. 123-128

Keyword(s):

Classification Scheme

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A Survey on Detection and Classification of Brain Tumor Using Image Processing Techniques

International Journal of Scientific Research in Computer Science Engineering and Information Technology ◽

10.32628/cseit2063211 ◽

2020 ◽

pp. 967-973

Author(s):

V. Deepika ◽

T. Rajasenbagam

Keyword(s):

Image Processing ◽

Brain Tumor ◽

Soft Tissues ◽

Tumor Detection ◽

Tumor Classification ◽

Normal Brain ◽

Image Processing Techniques ◽

Normal Brain Function ◽

Processing Techniques ◽

The Brain

A brain tumor is an uncontrolled growth of abnormal brain tissue that can interfere with normal brain function. Although various methods have been developed for brain tumor classification, tumor detection and multiclass classification remain challenging due to the complex characteristics of the brain tumor. Brain tumor detection and classification are one of the most challenging and time-consuming tasks in the processing of medical images. MRI (Magnetic Resonance Imaging) is a visual imaging technique, which provides a information about the soft tissues of the human body, which helps identify the brain tumor. Proper diagnosis can prevent a patient's health to some extent. This paper presents a review of various detection and classification methods for brain tumor classification using image processing techniques.

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