1. A detection apparatus for detecting the position of a boundary between a first part and a second part of a subject, the detection apparatus comprising:
a pixel extraction unit for extracting a plurality of candidate pixels acting as candidates for a pixel situated on the boundary on the basis of image data of a first section crossing the first part and the second part; and
a pixel specification unit for specifying the pixel situated on the boundary from within the plurality of candidate pixels by using an identifier which has been prepared by using an algorithm of machine learning.
2. The detection apparatus according to claim 1, wherein the identifier is prepared by making it learn supervised data by AdaBoost.
3. The detection apparatus according to claim 1, wherein the identifier is prepared by making it learn supervised data by Support Vector Machine.
4. The detection apparatus according to claim 1, wherein the pixel specification unit narrows down candidate pixels which are high in possibility that they are situated on the boundary from within the plurality of candidate pixels, and specifies the pixel which is situated on the boundary from within the narrowed-down candidate pixels by using the identifier.
5. The detection apparatus according to claim 1, further comprising:
a navigator region determination unit for determining the position of the navigator region on the basis of the specified pixel.
6. The detection apparatus according to claim 5,
wherein the pixel extraction unit extracts the plurality of candidate pixels per the first section on the basis of image data of a plurality of first sections crossing the first part and the second part,
the pixel specification unit specifies a set of pixels situated on the boundary per the first section, and
the navigator region determination unit selects a set of pixels to be used for determining the position of the navigator region from within the sets of pixels specified per the first section, and determines the position of the navigator region on the basis of the selected set of pixels.
7. The detection apparatus according to claim 6,
wherein the navigator region determination unit decides whether there exists a gap of pixels in the selected set of pixels, when there exists the gap of pixels, bridges the gap of pixels and determines the position of the navigator region on the bases of the set of pixels after the gap of pixels has been bridged.
8. The detection apparatus according to claim 7,
wherein the navigator region determination unit performs a fitting process on the set of pixels after the gap of pixels has been bridged and determines the position of the navigator region on the basis of the set of pixels after fitting-processed.
9. The detection apparatus according to claim 8,
wherein the navigator region determination unit determines a pixel situated at the uppermost position in the set of pixels after fitting-processed as the position of the navigator region.
10. The detection apparatus according to claim 1,
wherein image data of the first section is differentiated image data.
11. The detection apparatus according to claim 10,
wherein the pixel extraction unit obtains a profile of differential values of pixels on a line crossing the boundary using the differentiated image data and extracts the candidate pixels on the basis of the profile.
12. The detection apparatus according to claim 11,
wherein the pixel specification unit narrows down pixels which are high in possibility that they are situated on the boundary from within the two or more candidate pixels on the basis of pixel values of pixels situated around each candidate pixel on the line, in a case where two or more candidate pixels have been extracted on the line.
13. The detection apparatus according to claim 12,
wherein the pixel specification unit sets a first region and a second region for the candidate pixels, and narrows down pixels which are high in possibility that they are situated on the boundary on the basis of pixel values of pixels included in the first region and pixel values of pixels included in the second region.
14. The detection apparatus according to claim 1, wherein the pixel extraction unit obtains a search region including the boundary, and extracts the candidate pixels from within the search region.
15. The detection apparatus according to claim 14, wherein in a case where search region is to be obtained, image data of a second section intersecting with the first section is used.
16. The detection apparatus according to claim 15, wherein the first section is a coronal plane and the second section is an axial plane.
17. The detection apparatus according to claim 1, wherein the image data is image data that fat has been removed.
18. The detection apparatus according to claim 1, wherein the first part is the lung and the second part is the liver.
19. A magnetic resonance apparatus for detecting the position of a boundary between a first part and a second part of a subject, the magnetic resonance apparatus comprising:
a pixel extraction unit for extracting a plurality of candidate pixels acting as candidates for a pixel situated on the boundary on the basis of image data of a first section crossing the first part and the second part; and
a pixel specification unit for specifying the pixel situated on the boundary from within the plurality of candidate pixels by using an identifier which has been prepared by using an algorithm of machine learning.
20. A detection method of detecting the position of a boundary between a first part and a second part of a subject, the detection method comprising:
the pixel extraction step of extracting a plurality of candidate pixels acting as candidates for a pixel situated on the boundary on the basis of image data of a first section crossing the first part and the second part; and
the pixel specification step of specifying the pixel situated on the boundary from within the plurality of candidate pixels by using an identifier which has been prepared by using an algorithm of machine learning.
The claims below are in addition to those above.
All refrences to claim(s) which appear below refer to the numbering after this setence.
1. A computer implemented method for processing data in an electronic marketplace, the method comprising:
receiving documents sent through the electronic marketplace;
extracting data from the documents, wherein the extracted data relates to a predetermined statistical category of transactions conducted through the electronic marketplace and the extracted data for each document includes information identifying a document type;
storing the extracted data for each document;
aggregating the stored data according to the predetermined statistical category, wherein aggregating the stored data includes aggregating the stored data by document type to identify a quantity of documents for the predetermined statistical category;
receiving a query for a statistical category of data; and
presenting information from the appreciated data in response to the received query, wherein the presented information includes a number of documents sent through the electronic marketplace by an entity, a time period, and a document type associated with the documents.
2. The method of claim 1 further comprising filtering the documents to identify relevant documents prior to extracting data from the documents.
3. The method of claim 1 further comprising transforming each document from a format used by the electronic marketplace into a predefined format used for extracting data.
4. The method of claim 3 wherein transforming each document further comprises retrieving data from a master database and inserting the retrieved data into the document, wherein the retrieved data is selected based on information contained in the document.
5. The method of claim 1 wherein storing the extracted data for each document comprises:
identifying a transaction with which each document is associated; and
linking data from documents that are associated with the same transaction.
6. The method of claim 1 wherein the extracted data for each document includes information identifying at least one trading partner associated with the document and aggregating the stored data includes aggregating the stored data by trading partner.
7. The method of claim 1 wherein the extracted data for each document includes information identifying a date associated with the document and aggregating the stored data includes aggregating the stored data by time period.
8. The method of claim 1 wherein the documents sent through the electronic marketplace comprise sequences of documents having different document types, each sequence of documents relating to information for a corresponding transaction communicated between a first trading partner for the transaction and a second trading partner for the transaction.
9. A system for processing data in an electronic marketplace, the system comprising:
one or more processor executing an electronic marketplace; and one or more memory storing:
a database for storing documents transmitted via the electronic marketplace;
a data warehouse for storing statistical data relating to documents sent via the electronic marketplace, wherein information corresponding to predetermined statistical categories is extracted from the documents stored in the database to generate the statistical data and the statistical data comprises a number of documents transmitted via the electronic marketplace for at least one statistical category; and
a reporting application for accessing the data warehouse to retrieve statistical data and for generating reports representing aggregated statistical data, and for representing statistical data retrieved from the data warehouse in response to a query for a category of statistical data, wherein the presented information includes a number of documents transmitted via the electronic marketplace by an entity, a time period, and a document type associated with the documents.
10. The system of claim 9 further comprising a knowledge base for retrieving information corresponding to at least one predetermined statistical category based on data contained in the documents.
11. The system of claim 9 wherein the data warehouse includes an operational data storage repository for storing information on individual documents and an aggregated data repository for storing statistical data aggregated by statistical categories.
12. The system of claim 9 wherein the reporting application is operable to generate at least one report selected from the group consisting of a report relating to a number of documents transmitted via the electronic marketplace by document type and trading partner, a report relating to a number of documents transmitted between a pair of trading partners, and a report relating to a number of documents transmitted by document type.
13. The system of claim 9 wherein the electronic marketplace facilitates an exchange of documents between a first trading partner and a second trading partner.
14. An article comprising a machine-readable storage medium storing instructions operable to cause one or more machines to perform operations comprising:
receiving documents sent through an electronic marketplace;
extracting data from the documents, wherein the extracted data relates to a predetermined statistical category of transactions conducted through the electronic marketplace;
storing the extracted data;
aggregating the stored data according to the predetermined statistical category, wherein aggregating the stored data according to the predetermined statistical category comprises determining a number of documents transmitted through the electronic marketplace for the predetermined statistical category;
receiving a query for a statistical category of data; and
presenting information from the appreciated data in response to the received query, wherein the presented information includes a number of documents sent through the electronic marketplace by an entity, a time period, and a document type associated with the documents.
15. The article of claim 14 wherein the machine-readable medium stores instructions operable to cause one or more machines to perform operations further comprising storing additional information for each document, wherein the additional information is based on master data that corresponds to data contained in the document.
16. The article of claim 15 wherein a trading partner directory includes the master data, and the additional information includes a trading partner identifier associated with the document.
17. The article of claim 14 wherein the machine-readable medium stores instructions operable to cause one or more machines to perform operations further comprising generating a report based on the aggregated data.
18. The article of claim 17 wherein the machine-readable medium stores instructions operable to cause one or more machines to perform operations further comprising: receiving a request for additional detail relating to the report; and reporting on extracted data corresponding to individual documents that relate to the aggregated data.
19. The article of claim 14 wherein storing the extracted data comprises storing a date, a document type, and at least one trading partner identifier for each document.
20. The article of claim 19 wherein: the predetermined statistical category is defined by at least one parameter selected from the group consisting of a time period, a trading partner, a pair of trading partners, and a document type; and aggregating the stored data according to the predetermined statistical category comprises calculating a number of documents for the predetermined statistical category.
21. The article of claim 14 wherein the documents sent through the electronic marketplace comprise transaction information exchanged between a buying party and a selling party.