111111
`
`1111111111111111111111111111111111111111111111111111111111111
`US007050639B 1
`
`crz) United States Patent
`Barnes et al.
`
`(10) Patent No.:
`(45) Date of Patent:
`
`US 7,050,639 Bl
`May 23,2006
`
`(54)
`
`IMAGE DATA COMPRESSION EMPLOYING
`MULTIPLE COMPRESSION CODE TABLES
`
`(75)
`
`Inventors: Robert D. Barnes, Palatine, IL (US);
`Robert C. Gemperline, Algonquin, IL
`(US)
`
`(73) Assignee: General Electric Company,
`Schenectady, NY (US)
`
`( *) Notice:
`
`Subject to any disclaimer, the tenn of this
`patent is extended or adjusted under 35
`U.S.C. 154(b) by 0 days.
`
`(21) Appl. No.: 09/448,940
`
`(22) Filed:
`
`Nov. 24, 1999
`
`(51)
`
`(52)
`(58)
`
`Int. Cl.
`G06K 9136
`(2006.01)
`U.S.Cl.
`382/239; 382/238; 382/232
`Field of Classification Search
`382/239.
`382/238, 246, 243, 232, 247; 345/543, 544,
`345/555, 426; 341/107
`Sec application file for complete search history.
`
`(56)
`
`References Cited
`
`U.S. PATENT DOCUMENTS
`
`4,916,544 A
`5,253,078 A *
`5,289,548 A
`5,563,593 A *
`5,774,597 A
`5,801,716 A *
`5,883,979 A *
`
`. ......... 358/262.1
`4/1990 Lienard et al.
`10/1993 Balkanski et al.
`.......... 382/250
`2/1994 Wilson et al.
`................ 382/56
`10/1996 Puri .................. ...
`341/67
`6/1998 Wilson ............... ........ 382/250
`9/1998 Silverbrook ................ 345/506
`.............. 382/251
`3/1999 Beretta et a!.
`
`7/1999 Newton ...................... 382/247
`8/1999 Torborg et al.
`............. 345/555
`12/1999 Chadez ....................... 382/303
`........... 382/238
`5/2000 Kajiwara et a!.
`8/2000 Hirabayashi et al.
`....... 382/246
`4/2001 Martin ....................... 382/232
`6/2001 Nafarieh ..................... 382/253
`4/2002 Johns ......................... 345/543
`
`5,926,576 A *
`5,936,616 A *
`6,002,814 A *
`6,061,474 A *
`6,101,282 A *
`6,222,942 Bl *
`6,252,994 Bl *
`6,366,289 Bl *
`FOREIGN PATENT DOCUMENTS
`0755155 A2 * 1/1997
`EP
`0974933 A2 * 1/2000
`EP
`* cited by examiner
`Primary F:xaminer-Anh Hong Do
`(74) Attorney, Agent, or Firm-Fletcher Yoder
`
`(57)
`
`ABSTRACT
`
`An image data compression and decompression technique
`applies one or more compression code tables to optimally
`compress an image data stream. The compression code
`tables are established in accordance with anticipated image
`characteristics, and to accommodate different levels of
`variation or entropy in the image data. The image data may
`be divided into blocks or subregions for analysis of which of
`the candidate compression code tables provides the optimal
`compression of each subregion. The appropriate code table
`is selected for each subregion. TI1e evaluation of the com(cid:173)
`pression performance based upon application of each com(cid:173)
`pression code table may include analysis of prediction
`differences or errors between predicted values for pixels of
`an image and the actual values fur the pixels.
`
`27 Claims, 8 Drawing Sheets
`
`Vedanti Systems Limited - Ex. 2014
`Page 1
`
`

`
`U.S. Patent
`
`May 23,2006
`
`Sheet 1 of 8
`
`US 7,050,639 Bl
`
`---------------------------------------------------------
`1~
`
`28
`
`DB
`'----' SERVER
`
`FILE
`SERVER
`
`ARCHIVE
`
`J.
`
`).
`
`x~
`....
`
`24~
`PRINTER
`INTERFACE 1-
`
`26
`
`I
`
`p
`
`22~
`22~
`c
`
`c
`
`22
`)
`c
`-----?---------------------
`/
`
`10
`
`RIS-
`
`34
`
`INPUT I
`OUTPUT ~
`INTERFACE f-.
`
`~
`20 "\
`COMP I
`DECOMP
`INTERFACE
`
`+
`32
`-\... LIB
`--------------o---- --- ·--f-
`HIS
`EC
`
`(
`
`36
`
`/,...-----1--,
`
`38
`
`EC
`
`38 _____.,
`
`SYS 1
`
`SYS 2
`
`1~
`
`SYS 3
`
`FIG. 1
`
`Vedanti Systems Limited - Ex. 2014
`Page 2
`
`

`
`U.S. Patent
`
`May 23,2006
`
`Sheet 2 of 8
`
`US 7,050,639 Bl
`
`FIG. 2
`
`IMAGE
`~----1 FILE NAME
`'------'
`LOCATION
`
`46
`
`;:::::::::---------r---------------?---
`
`DESCRIPl
`
`:
`-------J
`
`40
`
`52
`
`54
`
`56
`
`62
`
`IMAGE 1
`
`DESCRIP2
`
`IMAGE 2
`
`DESCRIP3
`
`IMAGE 3
`
`I
`I
`
`~50
`_____ :.,. ____ .
`
`I
`
`FIG. 3
`
`(
`100
`
`102
`
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`Page 3
`
`

`
`U.S. Patent
`
`May 23,2006
`
`Sheet 3 of 8
`
`US 7,050,639 Bl
`
`FIG. 4
`
`~114
`
`INTENSITY
`
`124
`
`INTENSITY
`
`128
`
`124
`
`~~~~~+-~----~~~--~~~~~122
`
`~116
`
`120
`
`140
`
`142
`
`144
`
`P(i-l,j-1)
`
`P(i,j-1)
`
`P(i + l,j-1)
`
`P(i-1,j)
`
`P(i ,j)
`
`P(i + 1,j)
`
`146
`
`136
`
`148
`
`FIG. 5
`
`/
`
`138
`
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`
`

`
`U.S. Patent
`
`May 23,2006
`
`Sheet 4 of 8
`
`US 7,050,639 Bl
`
`I
`I
`I
`
`270
`274
`272
`250 j" --- -
`- - -- - --- --- -- - --- --- - -- -- - -- I
`I
`~ GENERATE
`GENERATE
`GENERATE
`:
`CODE
`PREDICTOR
`SUBREGION
`:
`1
`:
`TABLES
`PREFERENCES
`PREFERENCES
`:
`1-----------------l------------------
`I
`I
`.----------------- -----------------:
`252~ 276
`78 :
`2
`- RECEIVE
`RETRIEVE DESCRIPTION
`I/
`f--
`I
`IMAGE
`DATA FROM DB
`I
`I
`I
`•----------------- -----------------~
`_____ -~8p _________ -~~2 ____ 1 _3~~-----~8~- --------~8~-
`:
`AFFIX
`SELECT
`SELECT SUBSET
`1 .----L-----.
`SELECT
`REVIEW
`:
`I
`COMPRESSION 1
`SUBREGION
`OF COMPRESSION
`PREDICTOR($)
`: DESCRIPTION(S)
`I
`HEADER
`SIZE{S)
`TABLES
`I '------.....1
`I
`~--:r--------------------1-----------------------------J
`254
`290
`292
`294
`296
`------------- --------- -- ----------- --------- ____ ,
`I
`I
`I
`I
`:
`COMPUTE PREDICTION
`COMPRESS
`:
`1 ERRORS AND CANDIDATE
`SUBREGION
`1
`:
`LENGTHS
`:
`TABLES
`CODE
`L--:;r------------------r-----------------------------
`I
`I
`256
`298
`300
`302
`
`~--------- ---------------- ----------- -.
`
`INSERT
`:
`COMPRESSION
`INSERT PADDING
`:
`INSERT
`: END BLOCK CODE
`BITS
`CHECKSUM :
`-------r------------- --------------------
`I
`I
`258
`260
`
`FIG. 6
`
`266
`
`GET DECOMPRESS
`ALGORITHM FROM LIBRARY
`
`268
`
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`
`

`
`U.S. Patent
`
`May 23,2006
`
`Sheet 5 of 8
`
`US 7,050,639 Bl
`
`HUFFMAN
`CODE
`PREFIX
`
`16 -BIT LOSSLESS ENCODING
`MAX RATIO = 16:1
`SCHEME 0
`-
`CODE
`EXTENSION
`
`FIG. 7
`ENCODED
`RANGE
`DIFFERENCE d CODE LENGTH WIDTH
`
`18( ~~~- -----------X---------- _+_2 __ ii-~: ----r-------: ~ ,
`
`-2 TO -3
`1101
`X
`+ 4 TO + 7
`XX
`11100
`-4 TO -7
`XX
`11101
`111100 ~ +8 TO +15
`182
`111101
`XXX
`-8 TO -15
`1111100
`XXXX
`+16 TO +31
`1111101
`XXXX
`-16 TO -31
`11111100
`XXXXX
`+32 TO +63
`11111101
`XXXXX
`-32 TO -61
`111111100
`+ 64 TO + 127
`XXXXXX
`111111101
`-64 TO -127
`XXXXXX
`1111111100
`XXXXXXX
`+ 128 TO +255
`1111111101
`XXXXXXX
`-128 TO -255
`XXXXXXXX
`11111111100
`+ 256 TO + 511
`XXXXXXXX
`11111111101
`-256 TO -511
`t1 11111111110 < 16 BITS)
`ACTUAL VALUE
`' 11111111111
`END OF BLOCK
`172
`
`I
`174
`
`(
`176
`
`170
`
`+ 4
`
`+8
`
`+16
`
`+32
`
`+64
`
`+128
`
`+256
`
`5
`7
`7
`9
`9
`11
`11
`13
`13
`15
`15
`17
`17
`19
`19
`27
`11
`
`FIG. 8
`SCHEME 1 - MAX RATIO = 5.33:1
`HUFFMAN
`ENCODED
`CODE
`CODE
`RANGE
`PREFIX
`EXTENSION
`DIFFERENCE d CODE LENGTH WIDTH
`------------------------------------------------·
`X
`0 TO +1
`3
`1
`00
`X
`-1 TO -2
`3
`01
`+ 2 TO + 5
`100
`XX
`5
`101
`XX
`-3 TO -6
`5
`1100
`XXX
`+6 TO + 13
`7
`1101
`-7 TO -14
`XXX
`7
`11100
`+ 14 TO + 29
`9
`XXX/..
`11101
`-15 TO -30
`9
`XXX/..
`+ 30 TO + 61
`111100
`XXXXX
`11
`111101
`-31 TO -62
`11
`XXXXX
`+ 62 TO + 125
`1111100
`XXXXXX
`13
`1111101
`XXXXXX
`-63 TO -126
`13
`XXXXXXX
`11111100
`+ 126 TO + 253
`15
`XXXXXXX
`11111101
`-127 TO -254
`15
`XXXXXXXX
`111111100
`+254 TO +509
`17
`XXXXXXXX
`111111101
`-255 TO -510
`17
`1111111100
`XXXXXXXXX + 510 TO + 1021
`19
`1111111101
`XXXXXXXXX
`-511 TO -1022
`19
`1111111110 < 16 BITS)
`ACTUAL VALUE
`26
`1111111111
`END OF BLOCK
`10
`
`+128
`
`+256
`
`+512
`
`+4
`
`184
`+8
`+16 _J
`
`+32
`
`+64
`
`Vedanti Systems Limited - Ex. 2014
`Page 6
`
`

`
`U.S. Patent
`
`May 23,2006
`
`Sheet 6 of 8
`
`US 7,050,639 Bl
`
`SCHEME 2
`
`- MAX RATIO = 4:1
`
`FIG. 9
`ENCODED
`HUFFMAN
`RANGE
`CODE
`CODE
`DIFFERENCE d CODE LENGTH WIDTH
`EXTENSION
`PREFIX
`----------------------------------------------------- 186
`00
`XX
`0 TO + 3
`4
`3 ..---
`01
`XX
`4
`-1 TO -4
`XXX
`100
`+4 TO +11
`6
`XXX
`101
`-5 TO -12
`6
`XXXX
`1100
`+12 TO +27
`8
`XXXX
`8
`1101
`-13 TO -28
`XXXXX
`11100
`+28 TO +59
`10
`XXXXX
`-29 TO -60
`10
`11101
`XXXXXX
`111100
`+60 TO +123
`12
`XXXXXX
`111101
`-61 TO -124
`12
`XXXXXXX
`1111100
`+ 124 TO + 251
`14
`XXXXXXX
`1111101
`-125 TO -252
`14
`XXXXXXXX
`11111100
`+ 252 TO + 507
`16
`XXXXXXXX
`11111101
`-253 TO -508
`16
`111111100
`XXXXXXXXX + 508 TO + 1019
`18
`111111101
`XXXXXXXXX
`-509 TO -1020
`18
`111111110 < 16 BITS)
`ACTUAL VALUE
`25
`111111111
`END OF BLOCK
`9
`
`+8
`
`+16
`
`+32
`
`+64
`
`+ 128
`
`+ 256
`
`+ 512
`
`SCHEME 3
`
`- MAX RATIO = 3.2:1
`
`FIG. 10
`HUFFMAN
`ENCODED
`CODE
`CODE
`RANGE
`PREFIX
`EXTENSION
`DIFFERENCE d CODE LENGTH WIDTH
`-----------------------------------------------------
`0 TO + 7
`XXX
`5
`7
`00
`m
`xxx
`5
`~ro~
`100
`+8 TO +23
`XXXX
`7
`101
`-9 TO -24
`XXXX
`7
`1100
`+ 24 TO +55
`9
`XXXXX
`1101
`-25 TO -56
`XXXXX
`9
`11100
`XXXXXX
`+56 TO +119
`11
`11101
`XXXXXX
`-57 TO -120
`11
`111100
`XXXXXXX
`+120 TO +247
`13
`XXXXXXX
`111101
`-121 TO -248
`13
`1111100
`XXXXXXXX
`+ 248 TO + 503
`15
`1111101
`XXXXXXXX
`-249 TO -504
`15
`11111100
`XXXXXXXXX
`+ 504 TO + 1015
`17
`11111101
`XXXXXXXXX
`-505 TO -1016
`17
`11111110 <16 BITS)
`ACTUAL VALUE
`24
`11111111
`END OF BLOCK
`8
`
`--
`
`188
`
`+16
`
`+32
`
`+64
`
`+128
`
`+256
`
`+512
`
`Vedanti Systems Limited - Ex. 2014
`Page 7
`
`

`
`U.S. Patent
`
`May 23,2006
`
`Sheet 7 of 8
`
`US 7,050,639 Bl
`
`SCHEME 4
`
`- MAX RATIO = 2.67:1
`
`FIG. 11
`
`ENCODED
`HUFFMAN
`RANGE
`CODE
`CODE
`DIFFERENCE d CODE LENGTH WIDTH
`PREFIX
`EXTENSION
`----------------------------------------------------
`xxxx
`00
`6
`0 TO +15
`15
`xxxx
`01
`-1 TO -16
`6
`xxxxx
`100
`8
`+16 TO +47
`xxxxx
`101
`-17 TO -48
`8
`1100
`XJ..XXXX
`10
`+48 TO +111
`-49 TO -112
`XJ..XXXX
`1101
`10
`xxxxxxx
`11100
`12
`+112 TO +239
`xxxxxxx
`11101
`-113 TO -240
`12
`xxxxxxxx +240 TO +495
`111100
`14
`xxxxxxxx
`-241 TO -496
`111101
`14
`xxxxxxxxx +496 TO +1007
`1111100
`15
`xxxxxxxxx
`1111101
`-497 TO -1008
`16
`1111110 < 16 BITS)
`ACTUAL VALUE
`23
`1111111
`END OF BLOCK
`7
`
`190
`...,./
`
`+32
`
`+64
`
`+128
`
`+256
`
`+512
`
`SCHEME 5 - MAX RATIO = 2.29:1
`
`FIG.12
`ENCODED
`HUFFMAN
`RANGE
`CODE
`CODE
`JB~EI~ ______ -~!~f'!SJQ~ ____ QI~~E_R~~g~ ~ __ ~QQ~ _L~~~~H __ -~I.PJ!j ____ _
`xxxxx
`00
`7
`0 TO +31
`31
`__...192
`xxxxx
`01
`7
`-1 TO -32
`xxxxxx
`100
`9
`+32 TO +95
`xxxxxx
`101
`-33 TO -96
`9
`xxxxxxx
`1100
`11
`+96 TO +223
`xxxxxxx
`1101
`11
`-97 TO -224
`xxxxxxxx
`11100
`+224 TO +479
`13
`xxxxxxxx
`-225 TO -480
`11101
`13
`xxxxxxxx
`111100
`+480 TO +991
`15
`xxxxxxxx
`111101
`-481 TO -992
`15
`111110 < 16 BITS)
`22
`ACTUAL VALUE
`111111
`END OF BLOCK
`6
`
`+64
`
`+128
`
`+256
`
`+512
`
`Vedanti Systems Limited - Ex. 2014
`Page 8
`
`

`
`\0 = """""
`Ut = 0..,
`0
`-1
`00
`d
`
`~
`
`QC)
`~
`0
`QC)
`
`~ ....
`1J) =-~
`
`Q\
`Q
`Q
`N
`~w
`N
`'-<'!
`~
`~
`
`~ = !""t-
`
`!""t-
`~
`~
`
`IJJ .
`~ .
`
`I
`I
`
`212
`
`I I
`
`I
`
`208
`
`207
`
`206
`
`FIG. 13
`
`I
`:
`I
`I
`
`PAD
`
`224""\
`
`222\
`
`I· . I B~~ BL~~ ~DE!
`
`0 3 4 5
`11 10 01 00
`
`SUBREGION
`
`SIZE
`
`SELECTOR
`PREDICTOR
`
`200
`
`83
`
`j
`
`82
`
`j
`
`VERSION
`
`I
`
`194/ L
`
`I DESCRIP I I COMP I 81
`
`1i8 f-2o2 -f-2o4 -j
`
`I
`
`1T6
`
`I
`
`216
`
`214~
`
`Vedanti Systems Limited - Ex. 2014
`Page 9
`
`

`
`US 7,050,639 Bl
`
`1
`IMAGE DATA COMPRESSIO~ EMPLOYING
`MULTIPLE CO.\'IPRESSION CODE TABLES
`
`FIELD OF THE INVENTION
`
`2
`efficient manner. There is a particular need for a teclmique
`which can be applied to new and existing compression
`systems, and which can be adapted to various systems
`depending upon the characteristics of the images to be
`handled.
`
`SUMMARY OF THE INVENTION
`
`The present invention relates generally to a field of image
`compression and decompression, and more particularly to a
`teclmique for rapidly and optimally compressing and
`decompressing image data through the use of one or more
`compression code tables selected from a family of pre- 10
`defined tables
`
`BACKGROUND OF THE INVENTION
`
`Various techniques have been proposed and are currently
`in usc for analyzing and compressing large data files, such
`as image data files. Image data files typically include streams
`of data descriptive of image characteristics, typically of
`intensities or other characteristics of individual pixel ele(cid:173)
`ments or pixels in a reconstructed image. In the medical
`field, for example, large image files are typically created
`during an image acquisition or encoding sequence, such as
`in an x-ray system, a magnetic resonance imaging system, a
`computed tomography imaging system, and so forth. The
`image data is then processed, such as to adjust dynamic
`ranges, enhance certain features shown in the image, and so
`forth, for storage, transmittal and display.
`While image files may be stored in raw and processed
`formals, many image D.les are quite large, and would occupy
`considerable memory space. The increasing complexity of
`imaging systems also has led to the creation of very large
`image files, typically including more data as a result of the
`useful dynamic range of the imaging system and the size of
`the matrix of image pixels.
`In addition to occupying large segments of available
`memory, large image files can be difficult or time consuming
`to transmit from one location to another. In a typical medical
`imaging application, for example, a scanner or other imag(cid:173)
`ing device will typically create raw data which may be at
`least partially processed at the scanner. The data is then 40
`transmitted to other image processing circuitry, typically
`including a programmed computer, where the image data is
`further processed and enhanced. Ullimalely, lhe image data
`is stored either locally at the system, or in a picture archiving
`and communications system (PACS) for later retrieval and 45
`analysis. ln all of these data transmission steps, the large
`image data file must be accessed and transmitted from one
`device to another.
`Compression teclmiques have been developed that apply
`various algorithms and approaches to conversion of original 50
`image data to a compressed ±arm ±or transmission and
`storage. One such approach is based upon assignment of
`compressed data code by reference to a table, commonly
`referred to as a compression table. This approach is based on
`the probability (or the frequency) of occurrence of different 55
`levels, typically gray levels or intensity levels, for each pixel
`in an image, represented by corresponding binary values in
`the image data stream. In general, compression code table
`permits more frequently occurring values to be assigned a
`shorter compressed data code than less frequently occurring 60
`values. Compression ratios in such teclmiques may, how(cid:173)
`ever, be highly dependent upon lhe relative frequencies of
`occurrence of the values across the dynamic range of the
`image data.
`There is a present need for an improved teclmique for 65
`compressing and decompressing image data which provided
`higher relative compression ratios in a computationally
`
`The present invention provides an image data compres(cid:173)
`sion and decompression teclmique designed to respond to
`these needs. The technique is applicable to a wide variety of
`imaging fields, and is particularly well suited to medical
`diagnostic imaging systems. Compression in accordance
`with the teclmique is based upon reference to a set of
`15 compression tables, such as compression code tables which
`arc predefined to accommodate different levels to data
`variation or entropy in the image data stream. The compres(cid:173)
`sion code tables are stored for reference during the com(cid:173)
`pression process, and compression performance is evaluated
`20 by application of the candidate tables to detennine which
`table or tables provide the best compression.
`In a presently preferred embodiment, the family of com(cid:173)
`pression code tables offer differing compressed data code
`and differing compression ratios. The image data stream is
`25 analyzed in subregions to identifY which compression code
`table provides the best compression for each subregion. The
`appropriate code table is selected in accordance with this
`analysis, and several such tables may be selected for differ(cid:173)
`ent regions of lhe image data. Higher entropy regions, for
`30 example, may require application of tables providing some(cid:173)
`what reduced compression ratios, while lower entropy
`regions may use compression code tables providing much
`higher compression rations. The resulting overall compres(cid:173)
`sion ratio is tl1erefore improved as compared to teclmiques
`35 employing a single table for an entire image.
`
`BRIEF DESCRIPTION OF THE DRAWINGS
`
`FIG. 1 is a diagrannnatical representation of a picture
`archiving and conmnmication system or PACS for receiving
`and storing image data in accordance with certain aspects of
`lhe present leclmique;
`PIG. 2 is a diagrammatical representation of contents of
`a database for referencing stored image data in files con(cid:173)
`taining multiple image data sets, compressed data, and
`descriptive information;
`FIG. 3 is a representation of a typical image of the type
`received, compressed, and stored on the system of FIG. 1;
`FIG. 4 is a graphical representation of intensities of
`pixelated data across an image, subdivided into subregions
`for compression of the subregions optimally based upon
`characteristics of the subregions;
`FIG. 5 is a diagrammatical representation of a pixel
`neighborhood used in the analysis of the image data for
`compression purposes;
`FIG. 6 is a flow chart illustrating exemplary control logic
`for compressing and decompressing image data in accor(cid:173)
`dance with aspects of the present technique;
`FIGS. 7, 8, 9, 10, 11 and 12 are reference lookup tables
`in the form of compression code tables used to optimally
`compress subregions of image data in accordance with the
`present leclmique during lhe process illustrated in FIG. 6;
`and
`FIG. 13 is a diagrammatical representation of an exem(cid:173)
`plary image data set, including a descriptive header, a
`compression header, and blocks of compressed data by
`subregion.
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`3
`DETAILED DESCRIPTION OF THE
`INVENTION
`
`FIG. 1 illustrates a picture archive and communication
`system or PACS 10 fur receiving, compressing and decom(cid:173)
`pressing image data. In the illustrated embodiment, PACS 10
`receives image data from several separate imaging systems
`designated by reference numerals 12, 14 and 16. As will be
`appreciated by those skilled in the art, the imaging systems
`may be of various type and modality, such as magnetic
`resonance imaging (MRI) systems, computed tomography
`(CT) systems, positron emission tomography (PET) sys(cid:173)
`tems, radio fluoroscopy (RF), computed radiography (CR),
`ultrasound systems, and so forth. Moreover, the systems
`may include processing stations or digitizing stations, such
`as equipment designed to provide digitized image data based
`upon existing film or hard copy images. It should also be
`noted that the systems supplying the image data to the PACS
`may be located locally with respect to the PACS, sllch as in
`the same institution or facility, or may be entirely remote
`from the PACS, such as in an outlying clinic or affiliated
`institution. In the latter case, the image data may be trans(cid:173)
`mitted via any suitable network link, including open net(cid:173)
`works, proprietary networks, virtual private networks, and 25
`so forth.
`PACS 10 includes one or more file servers 18 designed to
`receive and process image data, and to make the image data
`available for decompression and review. Server 18 receives
`the image data through an input/output interface 19. Image
`data may be compressed in rolltines accessed through a
`compression/decompression interface 20. As described
`more fully below, interface 20 serves to compress the
`incoming image data rapidly and optimally, while maintain(cid:173)
`ing descriptive image data available for reference by server
`18 and other components of the PACS. Where desired,
`interface 20 may also serve to decompress image data
`accessed through the server. The server is also coupled to
`internal clients, as indicated at reference numeral 22, each
`client typically including a work station at which a radiolo(cid:173)
`gist, physician, or clinician may access image data from the
`server, decompress the image data, and view or output the
`image data as desired. Clients 22 may also input informa(cid:173)
`tion, such as dictation of a radiologist following review of
`examination sequences. Similarly, server 18 may be coupled
`to one or more interfaces, such as a printer interface 24
`designed to access and decompress image data, and to output
`hard copy images via a printer 26 or other peripheral.
`Server 28 may associate image data, and other work flow
`information within the PACS by reference to one or more
`file servers 18. In the presently contemplated embodiment,
`database server 28 may include cross-referenced informa(cid:173)
`tion regarding specific image sequences, referring or diag(cid:173)
`nosing physician information, patient information, back(cid:173)
`ground information, work list cross-references, and so forth.
`The intonnation within database server 28 serves to facili(cid:173)
`tate storage and association of the image data files with one
`another, and to allow requesting clients to rapidly and
`accurately access image data files stored within the system.
`Similarly, server 18 is coupled to one or more archives 30,
`such as an optical storage system, which serve as reposito(cid:173)
`ries oflarge volumes of image data for backup and archiving
`purposes. Teclmiques for transferring image data between
`server 18, and any memory associated with server 18
`forming a short term storage system, and archive 30, may 65
`follow any suitable data management scheme, such as to
`archive image data following review and dictation by a
`
`4
`radiologist, or after a sufficient time has lapsed since the
`receipt or review of the image files.
`In the illustrated embodiment, other components of the
`PACS system or institution may be integrated with the
`foregoing components to further enhance the system func(cid:173)
`tionality. For example, as illustrated in FIG. 1, a compres(cid:173)
`sion/decompression library 32 is coupled to interface 20 and
`serves to store compression routines, algorithms, look up
`tables, and so forth, for access by interface 20 (or other
`10 system components) upon execution of compression and
`decompression routines (i.e. to store various routines, soft(cid:173)
`ware versions, code tables, and so forth). In practice, inter(cid:173)
`face 20 may be part of library 32. Library 32 may also be
`collpled to other components of the system, sllch as client
`15 stations 22 or printer interface 24, serving similarly as a
`library or store for the compression and decompression
`routines and algorithms. Although illustrated as a separate
`component in FIG. L it should be understood that library 32
`may be included in any suitable server or memory device,
`20 including within server 18. Moreover, code defining the
`compression and decompression processes described below
`may be loaded directly into interface 20 and/or library 32, or
`may be loaded or updated via network links, including wide
`area networks, open networks, and so forth.
`Additional systems may be linked to the PACS, such as
`directly to server 28, or through interfaces such as interface
`19. In the embodiment illustrated in FIG. 1, a radiology
`department information system or RlS 34 is linked to server
`18 to facilitate exchanges of data, typically cross-referenc-
`30 ing data within database server 28, and a central or depart(cid:173)
`mental information system or database. Similarly, a hospital
`information system or HIS 36 may be coupled to server 28
`to similarly exchange database information, workflow infor(cid:173)
`mation, and so forth. Where desired, such systems may be
`35 interfaced through data exchange software, or may be par(cid:173)
`tially or fully integrated with the PACS system to provide
`access to data between the PACS database and radiology
`department or hospital databases, or to provide a single
`cross-referencing database. Similarly, external clients, as
`40 designated at reference numeral 38, may be interfaced with
`the PACS to enable images to be viewed at remote locations.
`Such external clients may employ decompression software,
`or may receive image files already decompressed by inter(cid:173)
`face 20. Again, links to such external clients may be made
`45 through any suitable connection, such as wide area net(cid:173)
`works, virtllal private networks, and so forth.
`FIG. 2 illustrates in somewhat greater detail the type of
`cross-referencing data made available to clients 20, 22, 24,
`30 through database server 28. The database entries, desig-
`so nated generally by reference numeral 40 in FIG. 2, will
`include cross-referenced information, including patient data
`42, references to specific studies or examinations 43, refer(cid:173)
`ences to specific procedures performed 44, references to
`anatomy imaged 45, and further references to specific image
`55 series 46 within the study or examination. As will be
`appreciated by those skilled in the art, sllch cross-referenced
`information may include further information regarding the
`time and date of the examination and series, the name of
`diagnosing, referring, and other physicians, the hospital or
`60 department where the images are created, and so forth. The
`database will also include address information identifying
`specific images, file names, and locations of the images as
`indicated at reference numeral 48. Where the PACS includes
`various associated memory devices or short term storage
`systems, these locations may be cross-referenced within the
`database and may be essentially hidden from the end user,
`the image files simply being accessed by the system for
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`5
`viewing from the specific storage location based upon
`cross-referenced infonnation in the database.
`As described more fully below, in accordance with certain
`aspects of the present technique, descriptive information is
`used to identify preferred or optimal compression routines
`used to compress image data. Such descriptive infonnation
`is typically available from header sections of an image data
`string, also as described in detail below. However, informa(cid:173)
`tion available from database server 28 may also serve as the
`basis for certain of the selections of the algorithms employed
`in the compression technique. Specifically database refer(cid:173)
`ences may be relied upon for identifYing such descriptive
`information as the procedures performed in an imaging
`sequence, specific anatomies or other teatures viewable in
`reconstructed images based upon the data, and so forth. Such
`information may also be available from the RIS 34 and from
`the HIS 36.
`FIG. 2 also illustrates an exemplary image file cross(cid:173)
`referenced by the database entries. As shown in FIG. 2,
`image file 50 includes a plurality of image data sets 52, 54
`and 56. In a typical image file, a large nlllllber of such image
`sets may be defined by a continuous data stream. Each data
`set may be compressed in accordance with specific com(cid:173)
`pression algorithms, including lossless compression algo(cid:173)
`rithms as described below, lossy compression algorithms,
`wavelet algorithms, and the preferred compression code
`table-based optimal compression algorithm described
`below.
`Within each image data set, a descriptive header 58 is
`provided, along with a compression header 60. TI1e headers
`58 and 60 are followed by compressed image data 62. The
`descriptive header 58 of each data set preferably includes
`industry-standard or recognizable descriptive information,
`such as DICOM compliant descriptive data. As will be
`appreciated by those skilled in the art, such descriptive
`information will typically include an identification of the
`patient, image, date of the study or series, modality of the
`system creating the image data, as well as additional infor(cid:173)
`mation regarding specific anatomies or features visible in the
`reconstructed images. As described more fully below, such
`descriptive header data is preferably employed in the present
`technique for identification of optimal compression algo(cid:173)
`rithms or routines used to compress the data within the
`compressed image data section 62. Data referring to the
`specific algorithm or routine used to compress the image
`data is then stored within compression header 60 for later
`reference in decompressing the image data. As described
`below, additional data is stored within the compressed image
`data, cross-referencing the algorithms identified in compres(cid:173)
`sion header 60 for use in decompressing the image data.
`Specifically, in a presently preferred embodiment, the com(cid:173)
`pression header 60 includes identification of the length of
`subregions of the compressed image data, as well as refer(cid:173)
`ences to specific optimal algorithms, in the form of com(cid:173)
`pression code tables used to compress the subregions opti(cid:173)
`mally.
`FIG. 3 illustrates a typical image which is encoded by
`packets of digitized data assembled in a continuous data
`stream which may be compressed and decompressed in the
`present techniques. The image, designated generally by the
`reference numeral 100, will typically include features of
`interest 102, such as specific anatomical features. In medical
`diagnostic applications, such features may include specific
`anatomies or regions of a patient viewable by virtue of the
`physics of the image acquisition modality, such as soft tissue
`in MRI system images, bone in x-ray images, and so forth.
`Each image is comprised of a matrix having a width 104 and
`
`6
`a height 106 defined by the number and distribution of
`individual pixels 108. The pixels of the itnage matrix are
`arranged in rows 110 and collllllns 112, and will have
`varying characteristics which, when viewed in the recon(cid:173)
`structed image, define the features of interest. In a typical
`medical diagnostic application, these characteristics will
`include gray level intensity or color. In the digitized data
`stream, each pixel is represented by binary code, with the
`binary code being appended to the descriptive header to aid
`10 in identification of the image and in its association with other
`images of a study. As noted above, such descriptive infor(cid:173)
`mation may include industry standard information, such as
`DICOM compliant data.
`FIG. 4 graphically represents intensities of pixel data
`15 defining an image across a pair of rows of the image matrix.
`Each row of the image matrix will include a series of pixels,
`each pixel being encoded by binary data descriptive of the
`pixel characteristics,
`typically
`intensity. Thus,
`lighter
`regions of the reconstructed image will correspond to pixels
`20 having a higher intensity level, with darker regions having
`a lower intensity level. \Vhen illustrated graphically, the
`intensity levels of the pixels across the image may form a
`contour or trace as shown in FIG. 4. Specifically, FIG. 4
`illustrates a first row 114lying adjacent to a second row 116,
`25 each row including a series of pixels having various inten(cid:173)
`sities as indicated by traces 118 and 120, respectively. As
`will be appreciated by those skilled in the art, in practice, the
`graph of the pixel intensities would form a step-wise func(cid:173)
`tion along the position axis 122 and having amplitudes
`30 varying along the intensity axis 124.
`It may be noted from FIG. 4 that in an actual image,
`variations in intensity along rows, and along columns as
`represented by correspondingly located intensities moving
`downwardly or upwardly through adjacent rows, will vary in
`35 accordance with the features represented in the recon(cid:173)
`structed image. As illustrated in FIG. 4, row 114 includes
`areas of rising and declining intensity, including areas of
`both low intensity and high intensity. Row 116 includes
`areas of similar intensity, but varying by virtue of the
`40 features represented in the image. In accordance with the
`present technique, the image data stream is reduced to
`subregions as represented generally by reterence numeral
`126 in FIG. 4. While the subregions may be of different
`lengths (i.e. numbers of pixels), in the presently preferred
`45 embodiment, each subregion includes data encoding an
`equal number of pixels. Those skilled in the art will readily
`recognize, however, that after compression the actual length
`of codes for the subregion will vary depending upon the
`intensity of the pixels within the subregion and the dynamic
`50 range of the digital data encoding the pixe